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Sharma, Suresh Kumar
- Effect of Repeated Administration of Cefquinome on Biochemical and Hematological Parameters in Buffalo Calves
Abstract Views :206 |
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Authors
Affiliations
1 Department of Veterinary Pharmacology and Toxicology, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, IN
1 Department of Veterinary Pharmacology and Toxicology, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, Punjab, IN
Source
Toxicology International (Formerly Indian Journal of Toxicology), Vol 22, No 1 (2015), Pagination: 110-113Abstract
Aim: Cefquinome, a fourth generation of cephalosporins have been developed for use in animals. Similar to other species, it may also have some adverse reactions in buffalo calves at therapeutic dosage. In the present study, effect of repeated administration of cefquinome on biochemical and hematological parameters was studied in buffalo calves. Materials and Methods: Animals were divided into two groups having three animals in each group. Group 1 was kept as control and animals of Group 2 were given cefquinome at dose rate of 2 mg.kg−1 body weight by intramuscular route for continuously 7 days. Blood samples were collected daily and 3 days post treatment. Results: The values of aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma‑glutamyl transpeptidase (GGTP), and alkaline phosphatase (ALKP) in control animals were in the range of 127.7–148.3 IU/L,49.0–55.6 IU/L, 14.0–17.3 IU/L, and 111.0–134.3 IU/L, respectively. The repeated administration of cefquinome did not influence the plasma activities of AST, ALT, GGTP, and ALKP in treated animals. The level of blood urea nitrogen (BUN) and creatinine before treatment was 14.3 ± 0.88 mg/dl and1.70 ± 0.04 mg/dl, which significantly increased on 3rd day (21.0 ± 1.53 mg/dl) and 2nd day (2.33 ± 0.07 mg/dl), respectively. Among hematological parameters, there was significant variation in levels of hemoglobin (Hb), total erythrocyte count (TEC), erythrocyte sedimentation rate (ESR), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC) in treated animals. No abnormal clinical symptoms were observed in any animal. Conclusion: The results revealed that clinically, the therapy of cefquinome may be continued up to 7 days.Keywords
Biochemical Effects, Buffalo Calves, Cefquinome, Hematological Effects.
- An Innovation Development to Eliminate the Red Eye Effects in Visual Image Processing Using Color Scheme Deep Learning Model
Abstract Views :115 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, IN
2 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
3 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical Universityin
4 Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra College of Agriculture, IN
1 Department of Computer Science and Technology, Madanapalle Institute of Technology & Science, IN
2 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical University, IN
3 Department of Computer Science and Engineering, D Y Patil Agricultural and Technical Universityin
4 Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra College of Agriculture, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2775-2780Abstract
Visual Image processing is seriously used to motion photograph, modeling, printed thing and placing articles on the Internet. There is a wide mass of options, methods, tools and implementing this process. The processing task of the image processing is to give them the most clearly and clearly real value or type, in which they are distorted. The preparation of the films from the image allows you to remove unwanted items brightly in the eyes. It is mainly to eliminate the effect of the red eye effect and drag the figure. In this paper an innovation model was proposed to eliminate the “Red Eye Effect (REE)”. This proposed method is based on the adjustment that is raised. The visual image processing is that the clarity of the image objects has increased. The film is mostly digital cameras default or threaded transfer color. White balance adjustment sliders can be used by heat. Some image processing programs and make this separate treatment is a purpose. Setting up different digital cameras will allow you to set the best expression in shooting. However, this is always possible. So it should be adjusted by a subsequent visual image processing.Keywords
Visual Image Processing, Motion Photograph, Red Eye Effect, Digital Cameras.References
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- Hybrid Deep Learning with Alexnet Feature Extraction and Unset Classification for Early Detection in Leaf Diseases
Abstract Views :62 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Engineering, UPL University of Sustainable Technology, IN
2 Department of Information Technology, Adhiyamaan College of Engineering, IN
3 Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra Agriculture University, IN
1 Department of Computer Engineering, UPL University of Sustainable Technology, IN
2 Department of Information Technology, Adhiyamaan College of Engineering, IN
3 Department of Statistics, Mathematics and Computer Science, Sri Karan Narendra Agriculture University, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 3 (2024), Pagination: 3255-3262Abstract
This study addresses the imperative need for early detection of leaf diseases in tobacco, pepper, and tomato plants, as these diseases significantly impact crop yield and quality. Existing methods often fall short in accurately identifying diseases across diverse plant species. The research aims to bridge this gap by proposing a hybrid deep learning approach, combining the robust feature extraction capabilities of AlexNet with the precise segmentation and classification prowess of UNet. The proposed hybrid model leverages AlexNet proficiency in extracting hierarchical features from plant leaf images and subsequently utilizes UNet for accurate disease classification. This synergistic combination enables the model to overcome the challenges posed by the varied morphologies of tobacco, pepper, and tomato leaves. Experimental results demonstrate the effectiveness of the proposed methodology, showcasing superior performance in terms of accuracy, sensitivity, and specificity compared to existing techniques. The hybrid deep learning approach exhibits promising potential for early and accurate detection of leaf diseases, contributing to sustainable crop management practices.Keywords
Leaf Disease Detection, Hybrid Deep Learning, AlexNet, UNet, Agriculture.References
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