http://localhost:8080/xmlui/handle/123456789/2978 Tue, 23 Jun 2026 06:26:54 GMT 2026-06-23T06:26:54Z http://localhost:8080/xmlui/handle/123456789/3546 Title: Pollen Classification of three types of plants of the family Malvaceae using Computational Intelligence Approach Authors: DHAWALE, VIJAY; DUDUL, SANJAY; TIDKE, JAYKIRAN Abstract: The Malvaceae family is a kind of plants, which are commonly found near habitat places in India. The earlier approaches reported in the literature were tedious and time consuming with less accuracy due to the similarity in shape and the exine sculptures of pollens. We describe a new classification approach for the classification of three types of pollen grains of the Malvaceae family based on feature vector comprised of Histogram coefficients and image statistics. The approach presented gives precise accuracy in classification of pollen grains of the same family by using SEM images Fri, 01 Jan 2021 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3546 2021-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3432 Title: Cuff-Less BP Stratification based on Bio-Signals processing using Machine Learning : An Investigative Study Authors: Shinde, Santosh; RajaRajeswari, Pothuraju Abstract: Cuff-Less Blood Pressure Stratification using Signal Processing with Machine Learning has gained immense attraction in the past decade among the research community. Blood Pressure, one of the most vital parameter of the human body representing overall well being of an individual. Most of the cardiovascular and cerebrovascular diseases (CCVD) including Hypertension are highly correlated to Blood Pressure. Existing BP measurement approaches are highly inconvenient and intermittent and do not allow continuous measurement of BP. Continuous BP measurement could prove to be significant indicator to most of the medicinal conditions and will lead to breakthrough achievement in the field of medical science. Cuff-Less Blood Pressure estimation hopefully can enable continuous blood pressure measurements in the time to come. A Plethora of methods for Cuff-Less BP Stratification have been experimented out by using Vital Bio Signals such PTT based, nPTT based, Machine Learning and Deep Learning based. Most of these methods leading to satisfactory beliefs that Cuff-Less BP estimation could be possible to the utmost accuracy for the diagnosis of most of the CCVD diseases as well as to monitor the overall well being of humans. However, most of the approaches still needs improvements, needs to be tested on a larger population with varying demographic features and real time application. This paper presents an investigative study of existing Cuff-Less BP Estimation approaches and discusses the merits and opportunities for improvements of the Cuff-Less BP Estimation methods. Mon, 05 Jul 2021 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3432 2021-07-05T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3431 Title: Time Line Correlative Spectral Processing for Stratification of Blood Pressure using Adaptive Signal Conditioning Authors: Shinde, Santosh; RajaRajeswari, Pothuraju Abstract: —Stratification of Blood Pressure is essential input in most of the cardiovascular diseases detection and prediction and is also a great aid to medical practitioners in dealing with Hypertension. Denoising based on spectral coding is developed based on frequency spectral decomposition and a spectral correlative approach based on wavelet transform. The existing approaches perform a standard deviation and mean of peak correlation in signal conditioning. The artifact filtrations were developed based on thresholding. Filtration of coefficients has an impact on accuracy of estimation and hence proper signal conditioning is a primal need. Wherein threshold is measured with discrete monitoring, time line observation could improve the accuracy of filtration efficiency under varying interference condition. Dynamic interference due to capturing or processing source results in jitter type noises which are short period deviations with varying frequency component. Hence a time frequency analysis for filtration is adapted for filtration. This paper presents an approach of spectral correlation approach for signal condition in stratification of blood pressure under cuff less monitoring. This presented approach operates on the spectral distribution of finer resolution bands for monitoring signal in denoising and decision making. Existing approaches lacks the capability of loss-less denoising which is efficiently worked out in this paper. Mon, 05 Jul 2021 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3431 2021-07-05T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3413 Title: PRELIMINARY STAGE AUTISM SPECTRUM DISORDER DETECTION Authors: Dongare, VikasL; Badwaik, MayurA; Bendkoli, KhushalV; Joshi, ParnaviS; Mahajan, MonaliP Abstract: Autism Spectrum Disorder (ASD) is a disorder related to brain development that impact the way a child perceives, learns, socializes and communicates with others. Although autism can be diagnosed at any age, it is said to be a developmental disorder because symptoms generally appear in the first two years of life and can persist till adulthood if not treated at same time. Child with autism suffers from limited behavior, interaction and communication. Diagnosing ASD can be difficult since there is no medical test, like a blood test, to diagnose the disorders. Specialists and doctors look at the child’s behavior and development for certain span of time to make a diagnosis. Previously, there were few methods for early detection like multiple screening tools, combined questionnaire and video capturing using Machine Learning Classifier. The proposed system will focus on early detection of ASD using Convolutional Neural Network (CNN). It intends to predict the level of the disorder among the infants at early stage so that they could get preliminary treatment. Taking images of the child as input, the system analyzes the image and determines the possibility of autism in the child. Sun, 01 May 2022 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3413 2022-05-01T00:00:00Z