Implementation of Deep CNN Model for the Detection of Plant Leaf Disease

The potato is the most important tuber crop in the world, and it is grown in about 125 different nations. Potato is the crop that is most commonly consumed by a billion people worldwide, virtually every day, behind rice and wheat. However, a number of bacterial and fungal diseases are causing the potato crop's quality and yield to decline. Potato Leaf diseases must be promptly identified and prevented to increase production. Various researchers look for solutions to protect plants instead of   traditional processes which take more time. Recent technological developments have thrown up many alternates to traditional methods which are labour intensive. The application of AlexNet model Deep Convolutional Neural Network(CNN) to recognise diseases in potato plants avoids the disadvantages of selecting disease spot features artificially and makes more objective the plant disease feature extraction. It improves research efficiency and speeds up technology transformation. Accuracies ranging from 85% - to 95% were obtained using AlexNet model Deep

Feature Extraction Methods by Various Concepts using SOM

Image retrieval systems gained traction with the increased use of visual and media data. It is critical to understand and manage big data, lot of analysis done in image retrieval applications. Given the considerable difficulty involved in handling big data using a traditional approach, there is a demand for its efficient management, particularly regarding accuracy and robustness. To solve these issues, we employ content-based image retrieval (CBIR) methods within both supervised , unsupervised pictures. Self-Organizing Maps (SOM), a competitive unsupervised learning aggregation technique, are applied in our innovative multilevel fusion methodology to extract features that are categorised. The proposed methodology beat state-of-the-art algorithms with 90.3% precision, approximate retrieval precision (ARP) of 0.91, and approximate retrieval recall (ARR) of 0.82 when tested on several benchmark datasets

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Enhancing human sight perceptions to optimize machine vision: Untangling object recognition using deep learning techniques

The goal of machine vision is to develop human-like visual abilities; however, it is unclear whether understanding human vision will advance machines. Here, it exemplifies two key conceptual advancements: It first shows that the majority of computer vision models consistently differ from the way that individuals perceive objects. To do this, a significant dataset of human perceptions of the separations of isolated things was acquired, and it was then examined to see if a well-known machine vision algorithm can predict these perceptions. The best algorithms can account for the majority of the volatility in the intuitive data, but every algorithm we verified repeatedly misjudged several different object types. Second, it shows that removing these systemic biases can considerably increase classification accuracy. For instance, machine techniques overestimated detachments between symmetric objects in comparison to human vision. These results illustration that methodical differences between human and machine vision can be identified and improved.In order to improve the machine vision, employing a deep learning algorithm Visual Geometry Group (VGG 16) with planar reflection symmetry (PRS-Net) technique. VGG 16 is a convolutional neural network with 16 deep layers. VGG pre-trained architecture can point out visual features present in the image. The planar reflection symmetry concept is appended with VGG to create a hybrid environment that can improve machine vision significantly by 90%

Fabrication of Minerals Substituted Porous Hydroxyapaptite/Poly(3,4-ethylenedioxy pyrrole-<i>co</i>-3,4-ethylenedioxythiophene) Bilayer Coatings on Surgical Grade Stainless Steel and Its Antibacterial and Biological Activities for Orthopedic Applications

Current strategies of bilayer technology have been aimed mainly at the enhancement of bioactivity, mechanical property and corrosion resistance. In the present investigation, the electropolymerization of poly­(3,4-ethylenedioxypyrrole-<i>co</i>-3,4-ethylenedioxythiophene) (P­(EDOP-<i>co</i>-EDOT)) with various feed ratios of EDOP/EDOT on surgical grade stainless steel (316L SS) and the successive electrodeposition of strontium (Sr²⁺), magnesium (Mg²⁺) and cerium (Ce³⁺) (with 0.05, 0.075 and 0.1 M Ce³⁺) substituted porous hydroxyapatite (M-HA) are successfully combined to produce the bioactive and corrosion resistance P­(EDOP-<i>co</i>-EDOT)/M-HA bilayer coatings for orthopedic applications. The existence of as-developed coatings was confirmed by Fourier transform-infrared spectroscopy (FT-IR), X-ray diffraction (XRD), proton nuclear magnetic resonance spectroscopy (¹H NMR), high resolution scanning electron microscopy (HRSEM), energy dispersive X-ray analysis (EDAX) and atomic force microscopy (AFM). Also, the mechanical and thermal behavior of the bilayer coatings were analyzed. The corrosion resistance of the as-developed coatings and also the influence of copolymer (EDOP:EDOT) feed ratio were studied in Ringer’s solution by electrochemical techniques. The as-obtained results are in accord with those obtained from the chemical analysis using inductively coupled plasma atomic emission spectrometry (ICP-AES). In addition, the antibacterial activity, <i>in vitro</i> bioactivity, cell viability and cell adhesion tests were performed to substantiate the biocompatibility of P­(EDOP-<i>co</i>-EDOT)/M-HA bilayer coatings. On account of these investigations, it is proved that the as-developed bilayer coatings exhibit superior bioactivity and improved corrosion resistance over 316L SS, which is potential for orthopedic applications

Direct Fabrication of Functional and Biofunctional Nanostructures Through Reactive Imprinting

One-step reactive imprinting of a protected maleimide polymer provides a nanopatterned maleimide surface via a retro-Diels-Alder reaction. The patterned surfaces are used as scaffolds for the generation of functional and biofunctional structures. The biofunctional surface offers a platform for aligning the cells in the direction of patterns, demonstrating the potential for applications in the field of tissue engineering