A Review of Image Super Resolution using Deep Learning

The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designed, image enhancement used or not, among other factors. To improve the accuracy of the image super resolution, a different deep learning techniques might be explored. In fact, greater research into the image super resolution in medical imaging is possible to improve the data's suitability for future analysis. In light of this, it can be said that there is a lot of scope for research in the field of medical imaging

Image Based Model for Document Search and Re-ranking

Traditional Web search engines do not use the images in the web pages to search relevant documents for a given query. Instead, they are typically operated by computing a measure of agreement between the keywords provided by the user and only the text portion of each web page. This project describes whether the image content appearing in a Web page can be used to enhance the semantic description of Web page and accordingly improve the performance of a keyword-based search engine. A Web-scalable system is presented in such a way that exploits a pure text-based search engine that finds an initial set of candidate documents as per given query. Then, by using visual information extracted from the images contained in the pages, the candidate set will be re-ranked. The computational efficiency of traditional text-based search engines will be maintained by the resulting system with only a small additional storage cost that will be needed to predetermine the visual information

Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification

An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide range of license plate designs, fonts, and lighting situations.We incorporate data augmentation approaches to accommodate differences in license plate orientation, scale, and perspective throughout the training process to improve recognition accuracy. Additionally, we use transfer learning to enhance the system's generalization abilities by refining the pre-trained model on a sizable dataset.A trustworthy and effective solution for vehicle identification duties is provided by the Deep Learning-Based Automatic Vehicle License Plate Recognition System. Deep learning approaches are used to guarantee precise and instantaneous recognition, making it suitable for many uses such as law enforcement, parking management, and intelligent transportation systems

Aspergillus Salpingitis: A Rare Case Report

We describe the pathology of a unique case of fallopian tube aspergillosis in a 45 year old woman. She complained of lower abdominal pain and lump in lower abdomen since 2-3 months. Clinically she was diagnosed as benign ovarian tumor, right ovary. Pathological examination showed dilated fallopian tube containing yellow material. Microscopic examination showed Aspergillous filaments surrounded by dense infiltrate of neutrophils and lymphocytes. Even though Aspergillous salpingitis is a rare entity, the correct diagnosis is of great importance for the indication of proper therapy.KEY WORDS: America, Aspergillus, aspergillosis, salpingiti

Microglia in aging and Alzheimer’s disease: A comparative species review

Microglia are the primary immune cells of the central nervous system that help nourish and support neurons, clear debris, and respond to foreign stimuli. Greatly impacted by their environment, microglia go through rapid changes in cell shape, gene expression, and functional behavior during states of infection, trauma, and neurodegeneration. Aging also has a profound effect on microglia, leading to chronic inflammation and an increase in the brain’s susceptibility to neurodegenerative processes that occur in Alzheimer’s disease. Despite the scientific community’s growing knowledge in the field of neuroinflammation, the overall success rate of drug treatment for age-related and neurodegenerative diseases remains incredibly low. Potential reasons for the lack of translation from animal models to the clinic include the use of a single species model, an assumption of similarity in humans, and ignoring contradictory data or information from other species. To aid in the selection of validated and predictive animal models and to bridge the translational gap, this review evaluates similarities and differences among species in microglial activation and density, morphology and phenotype, cytokine expression, phagocytosis, and production of oxidative species in aging and Alzheimer’s disease

A clinical study of arrhythmias associated with acute myocardial infarction and thrombolysis

Background: Arrhythmias are a common occurrence in ACS. This study was undertaken to analyze the incidence, frequency and type of arrhythmias in relation to the site of infarction to aid in timely intervention to modify the outcome in MI and to study the significance of Reperfusion arrhythmias.Methods: 100 patients were evaluated. ECG and cardiac enzymes were studied. Arrhythmias complicating AMI in terms of their incidence, timing, severity, type, relation, reperfusion and results were studied.Results: Of the 100 cases, 74% were males and 26% females of which incidence being common between 4th to 7th decades of life. AMI was common in patients with Diabetes and Hypertension (23% each). Incidence of AWMI (58%) is higher than IWMI (40%). Out of all arrhythmias, Ventricular Tachycardia was seen in 24% cases with 50% mortality and preponderance to Antero Lateral Myocardial Infarction. Sinus Tachycardia was seen in 23% of cases with preponderance to Antero Lateral Myocardial Infarction and persistence of Sinus Tachycardia was a prognostic sign, mortality being 22%. Complete Heart Block and Sinus Bradycardia were seen with IWMI, incidence being 53.8% and 100% respectively. Bundle Branch Block was common in AWMI (31%) than IWMI (10%). Among 64 thrombo-lysed cases, 21 had Reperfusion Arrhythmias without any mortality, whereas remaining 43 without Reperfusion Arrhythmias had mortality of 18.6%.Conclusions: According to the study, Tachy-arrhythmias are common with Anterior Wall Myocardial Infarction and Brady-arrhythmias in Inferior Wall Myocardial Infarction. Reperfusion Arrhythmias are a benign phenomenon and good indicator of successful reperfusion

Report on morphological abnormality in Scylla serrata

Morphological abnormalities most commonly reported in crabs are alterations in carapace (mainly number and shape of antero-lateral teeth), chelipeds, walking legs and shape of the abdomen. Uran, a fishing village in Raigad district of Maharashtra, supports a good fishery of Scylla serrata commonly known as giant mud crab, found in the coastal estuarine and mangrove areas