Purushottam Sharma

Bio: Purushottam Sharma is an academic researcher from Amity University. The author has contributed to research in topics: Ad hoc On-Demand Distance Vector Routing & Routing protocol. The author has an hindex of 10, co-authored 50 publications receiving 279 citations. Previous affiliations of Purushottam Sharma include Guru Gobind Singh Indraprastha University & Rajiv Gandhi Proudyogiki Vishwavidyalaya.

A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering

Abstract: As per recent developments in medical science, the skin cancer is considered as one of the common type disease in human body. Although the presence of melanoma is viewed as a form of cancer, it is challenging to predict it. If melanoma or other skin diseases are identified in the early stages, prognosis can then be successfully achieved to cure them. For this, medical imaging science plays an essential role in detecting such types of skin lesions quickly and accurately. The application of our approaches is to improve skin cancer detection accuracy in medical imaging and further, can be automated using electronic devices such as mobile phones etc. In the proposed paper, an improved strategy to detect three type of skin cancers in early stages are suggested. The considered input is a skin lesion image which by using the proposed method, the system would classify it into cancerous or non-cancerous type of skin. The image segmentation is implemented using fuzzy C-means clustering to separate homogeneous image regions. The preprocessing is done using different filters to enhance the image attributes while the other features are assessed by implementing rgb color-space, Local Binary Pattern (LBP) and GLCM methods altogether. Further, for classification, artificial neural network (ANN) is trained using differential evolution (DE) algorithm. Various features are accurately estimated to achieve better results using skin cancer image datasets namely HAM10000 and PH2. The novelty of the work suggests that DE-ANN is best compared among other traditional classifiers in terms of detection accuracy as discussed in result section of this paper. The simulated result shows that the proposed technique effectually detects skin cancer and produces an accuracy of 97.4%. The results are highly accurate compare to other traditional approaches in the same domain.

Heart Disease Prediction using Evolutionary Rule Learning

Abstract: In modern society, Heart disease is the noteworthy reason for short life. Large population of people depends on the healthcare system so that they can get accurate result in less time. Large amount of data is produced and collected by the healthcare organization on the daily basis. To get intriguing knowledge, data innovation permits to extract the data through automization of processes. Weighted Association Rule is a type of data mining technique used to eliminate the manual task which also helps in extracting the data directly from the electronic records. This will help in decreasing the cost of services and also helps in saving lives. In this paper, we will find the rule to predict patient's risk of having coronary disease. Test results have shown that vast majority of the rules helps in the best prediction of coronary illness.

Forecasting major impacts of COVID-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap.

Abstract: The pandemic caused by the coronavirus disease 2019 (COVID-19) has produced a global health calamity that has a profound impact on the way of perceiving the world and everyday lives. This has appeared as the greatest threat of the time for the entire world in terms of its impact on human mortality rate and many other societal fronts or driving forces whose estimations are yet to be known. Therefore, this study focuses on the most crucial sectors that are severely impacted due to the COVID-19 pandemic, in particular reference to India. Considered based on their direct link to a country's overall economy, these sectors include economic and financial, educational, healthcare, industrial, power and energy, oil market, employment, and environment. Based on available data about the pandemic and the above-mentioned sectors, as well as forecasted data about COVID-19 spreading, four inclusive mathematical models, namely-exponential smoothing, linear regression, Holt, and Winters, are used to analyse the gravity of the impacts due to this COVID-19 outbreak which is also graphically visualized. All the models are tested using data such as COVID-19 infection rate, number of daily cases and deaths, GDP of India, and unemployment. Comparing the obtained results, the best prediction model is presented. This study aims to evaluate the impact of this pandemic on country-driven sectors and recommends some strategies to lessen these impacts on a country's economy.

Content based image retrieval using a neuro-fuzzy technique

Abstract: In this paper, we propose a neuro-fuzzy technique for content based image retrieval. The technique is based on fuzzy interpretation of natural language, neural network learning and searching algorithms. Firstly, fuzzy logic is developed to interpret natural expressions such as mostly, many and few. Secondly, a neural network is designed to learn the meaning of mostly red, many red and few red. The neural network is independent to the database used, which avoids re-training of the neural network. Finally, a binary search algorithm is used to match and display neural network's output and images from database. The proposed technique is very unique and the originality of this research is not only based on hybrid approach to content based image retrieval but also on the new idea of training neural networks on queries. One of the most unique aspects of this research is that neural network is designed to learn queries and not databases. The technique can be used for any real-world online database. The technique has been implemented using CGI scripts and C programming language. Experimental results demonstrate the success of the new approach.

A region-based fuzzy feature matching approach to content-based image retrieval

Abstract: This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.

Evidence and Implications of Mortality Associated with Acute Plasmodium vivax Malaria

Abstract: Vivax malaria threatens patients despite relatively low-grade parasitemias in peripheral blood. The tenet of death as a rare outcome, derived from antiquated and flawed clinical classifications, disregarded key clinical evidence, including (i) high rates of mortality in neurosyphilis patients treated with vivax malaria; (ii) significant mortality from zones of endemicity; and (iii) the physiological threat inherent in repeated, very severe paroxysms in any patient, healthy or otherwise. The very well-documented course of this infection, with the exception of parasitemia, carries all of the attributes of “perniciousness” historically linked to falciparum malaria, including severe disease and fatal outcomes. A systematic analysis of the parasite biomass in severely ill patients that includes blood, marrow, and spleen may ultimately explain this historic misunderstanding. Regardless of how this parasite is pernicious, recent data demonstrate that the infection comes with a significant burden of morbidity and associated mortality. The extraordinary burden of malaria is not heavily weighted upon any single continent by a single species of parasite—it is a complex problem for the entire endemic world, and both species are of fundamental importance. Humanity must rally substantial resources, intellect, and energy to counter this daunting but profound threat.

A Survey On: “Content Based Image Retrieval Systems”

Abstract: network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analysed and compared. It also introduced the feature like neuro fuzzy technique, color histogram, texture and edge density for accurate and effective Content Based Image Retrieval System.