Texture Analysis of a Color Image Using Traditional and Circular Gabor Filters

According to computer vision, segmentation is defined as the process of partitioning a digital image into multiple segments, where multiple segments are sets of pixels, in other words super pixels. Main objective of segmentation is to change and, or simplify the representation of a digital image into something that is much more significant and easier to analyze. Objects and boundaries like lines, curves, etc. in images can be normally located by using image segmentation. More accurately, the process of assigning a tag to every pixel in an image such that pixels with the same label share specific visual characteristics is known as image segmentation. The outcome of image segmentation is a set of surface ( especially of a curving form ) extracted from the image, a set of segments that as a group cover the entire image. In a segment every pixels are similar with regard to computed property or some characteristic, such as intensity, texture, or color. A Gabor filter is a linear filter used for edge detection in image processing which is named after Dennis Gabor. Gabor filter frequency and orientation representations are similar to those of human visual system, for texture representation and discrimination it has been found to be remarkably appropriate. Gabor filter is a powerful tool in texture analysis. Traditional Gabor function ( TGF ) represents a Gaussian function modulated with the help of an oriented complex sinusoidal signal

FEDRESOURCE: Federated Learning Based Resource Allocation in Modern Wireless Networks

Deep reinforcement learning can effectively deal with resource allocation (RA) in wireless networks. However, more complex networks can have slower learning speeds, and a lack of network adaptability requires new policies to be learned for newly introduced systems. To address these issues, a novel federated learning-based resource allocation (FEDRESOURCE) has been proposed in this paper which efficiently performs RA in wireless networks. The proposed FEDRESOURCE technique uses federated learning (FL) which is a ML technique that shares the DRL-based RA model between distributed systems and a cloud server to describe a policy. The regularized local loss that occurs in the network will be reduced by using a butterfly optimization technique, which increases the convergence of the FL algorithm. The suggested FL framework speeds up policy learning and allows for adoption by employing deep learning and the optimization technique. Experiments were conducted using a Python-based simulator and detailed numerical results for the wireless RA sub-problems. The theoretical results of the novel FEDRESOURCE algorithm have been validated in terms of transmission power, convergence of algorithm, throughput, and cost. The proposed FEDRESOURCE technique achieves maximum transmit power up to 27%, 55%, and 68% energy efficiency compared to Scheduling policy, Asynchronous FL framework, and Heterogeneous computation schemes respectively. The proposed FEDRESOURCE technique can increase discrimination accuracy by 1.7%, 1.2%, and 0.78% compared to the scheduling policy framework, Asynchronous FL framework, and Heterogeneous computation schemes respectively

Perforated transverse vaginal septum: a rare case report

20 year old nulliparous woman married since 1 ½ years attended the gynaecology OPD with history of inability to conceive. She had regular menstrual cycles with normal menstrual flow. Local examination revealed blind vagina with a small opening in the centre. On per rectal examination, uterus was felt and normal in size. Based on history and clinical examination finding, a provisional diagnosis of perforated transverse vaginal septum was made. MRI revealed transverse vaginal septum in the lower 1/3rd of vagina with a small fenestration without haematocolpos or haematometra. Transverse vaginal septum resection was done. Vagina healed well without stricture formation. Transverse vaginal septum in the lower 1/3rd of vagina (perforating type) is a rare entity and hence it is presented

Reliable and Automatic Recognition of Leaf Disease Detection using Optimal Monarch Ant Lion Recurrent Learning

Around 7.5 billion people worldwide depend on agriculture production for their livelihood, making it an essential component in keeping life alive on the planet. Negative impacts are being caused on the agroecosystem due to the rapid increase in the use of chemicals to combat plant diseases. These chemicals include fungicides, bactericides, and insecticides. Both the quantity and quality of the output are impacted when there is a high-scale prevalence of diseases in crops. Plant diseases provide a significant obstacle for the agricultural industry, which has a negative impact on the growth of plants and the output of crops. The problem of early detection and diagnosis of diseases can be solved for the benefit of the farming community by employing a method that is both quick and reliable regularly. This article proposes a model for the detection and diagnosis of leaf infection called the Automatic Optimal Monarch AntLion Recurrent Learning (MALRL) model, which attains a greater authenticity. The design of a hybrid version of the Monarch Butter Fly optimization algorithm and the AntLion Optimization Algorithm is incorporated into the MALRL technique that has been proposed. In the leaf image, it is used to determine acceptable aspects of impacted regions. After that, the optimal characteristics are used to aid the Long Short Term Neural Network (LSTM) classifier to speed up the process of lung disease categorization. The experiment's findings are analyzed and compared to those of ANN, CNN, and DNN. The proposed method was successful in achieving a high level of accuracy when detecting leaf disease for images of healthy leaves in comparison to other conventional methods

Comparative prospective randomized open label trial of synbiotic (bifilac) as an add on therapy with standard treatment in patients with aphthous ulcer

Background: To trial the safety, efficacy and rapidity of response to a lozenges containing synbiotic in patients with minor aphthous ulcer.Methods: A total of 60 patients were enrolled for the trial after obtaining IEC approval and randomly allocated into two groups. Control “Group A” was administered with conventional treatment i.e., zytee and B complex for 2 weeks and trial “Group B” was administered with Bifilac along with conventional treatment for 2 weeks. The results of this trial were analyzed both subjectively and objectively.Results: Comparing with control group, where standard treatment was used with analgesics and B-complex, the trial group showed a quick relief of pain and helped in reducing mean size of ulcer.Conclusions: This trial was done with synbiotic lozenges in minor aphthous ulcers and it proved to be better alternative for them. Moreover, synbiotics have no adverse effects