R. Sathya

SRM Institute of Science and Technology

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Prasad Rasane

Lovely Professional University

Mahendra Gunjal

Lovely Professional University

Jyoti Singh

Lovely Professional University

Vishesh Bhadariya

Oklahoma State University Oklahoma City

Jaspreet Kaur

Punjab Agricultural University

Sawinder Kaur

Lovely Professional University

M. Kalaiselvi Geetha

Manipal Academy of Higher Education

T. Ushadevi

PRIST University

Mallikharjuna Rao Nuka

National Institute of Technology Andhra Pradesh

B. Saleena

Vellore Institute of Technology University

Cooperative Institutions

SRM Institute of Science and Technology

Anna University, Chennai

Annamalai University

Lovely Professional University

Bharathidasan University

Vellore Institute of Technology University

St. Joseph’s College of Engineering

St. Joseph's Institute of Technology

Indian Institute of Technology Kharagpur

Universidade Federal do Paraná

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In Vitro Anti‐Plasmodial Activity of Trigonella foenum–graecum L.

Evidence-based Complementary and Alternative Medicine (2008)

M. Palaniswamy B. V. Pradeep R. Sathya Jayaraman Angayarkanni

Developing countries, where malaria is one of the most prevalent diseases, still rely on traditional medicine as a source for the treatment of this disease. For the present study, Trigonella foenum-graecum L. (fenugreek) were collected from Coimbatore, Tamilnadu, India. The test plant has been used in India by traditional healers for the treatment of fever as well as other diseases. The active principle was extracted out in different solvent systems to assess the anti-plasmodial potential, with an aim that they can further be utilized to formulate drugs. In vitro anti-plasmodial assay of the extracted fractions of fenugreek leaves was carried out using laboratory adapted chloroquine sensitive and resistant Plasmodium falciparum isolates. Schizont maturation inhibition assay was adopted to analyze the potential of the extracts. Ethanol extract (50%) seemed to possess profound anti-plasmodial activity with IC(50) value of 8.75 ± 0.35 µg ml(-1) and 10.25 ± 0.35 µg ml(-1) against chloroquine sensitive and resistant P. falciparum isolates, respectively. Among the investigated six fractions of the plant extracts, two were found to have significant anti-plasmodial activity with IC(50) values <10 µg ml(-1), namely ethanol and butanol extracts. Two extracts chloroform and ethyl acetate showed moderate activity with IC(50) values ranging from 10 to 20 µg ml(-1), and the other two extracts, hexane and water appeared to be inactive with IC(50) values >85 µg ml(-1). In addition, preliminary phytochemical screening of the various extracts indicated the presence of alkaloids, saponin, tannin like phenolic compounds, flavonoids and steroids.

Trigonella

10.1093/ecam/nen030

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Citations (22)

Particle Filtering Framework and OcclusionHandling Using Region Based Tracking Techniquefor 2-D and 3-D Image Sequences

International Journal of Innovative Research in Computer and Communication Engineering (2014)

Jyoti Rani R. Sathya R. D. Sathiya

3 ABSTRACT: A method is introduced to address the estimation of pose for 2D-3D images. This approach involves the tracking of 2D images in 3D spaces which results in the estimation of pose with the continuous process of particle filtering and handling the scheme in the presence of occlusion. In order to maintain the track and to detect the exact region, a unique method Region Based Tracking method is used in this paper. It also involves the degree of dependencies between predictions and measurements model which determines the position and estimation of the object sustaining particle filters and handling occlusions. Particle filtering can propagate via autoregressive model for tracking 2D images but also estimating a 3D pose. Finally, this method is considered to be more effective in both robustness and speed compared to similar video tracking and pose estimation methods. Thus the conversion of pose estimation from 2D Silhouette curve to 3D Occluding Curve is finally illuminated in video.

Robustness

Tracking (education)

Source

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Citations (1)

Fake and Unproven Medical Remedy Detector using LSTM Model with Tensorflow Framework

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (2022)

R. Sathya Sayanti Roy Jibendu Paul Mopuru Sahithi Reddy

Social media for imposter content consumption is perilous to civilization. On a single hand, it is budget friendly, free to access speedy spread of details and shows people to devour loads of news from social platforms. On the flip side, it authorizes the huge spread of “proven and unproven medical remedies”, that is bad quality remedies consisting intentionally of fake information. The rapid spread of false medical remedies has innumerous negative impacts over individuals in society. Thus, fake medical remedies on social media have just appeared to be growing research. Fake remedies can mislead users for believing in the false information, which represent it as quite hard and nontrivial to analyze the correctness of news. So, auxiliary information, like user social engagements should be added on social media, to help make resolutions. In our work we primarily focus on analyzing and detecting the fake and unproven medical remedies and provide the correct remedies to the people and stop them from believing in false and unproven ones using ML Model. The data obtained from social media can be used to understand and detect the fake medical remedies. People sometimes believe in superstitions and fake and cheap medical remedies. Such misapprehension can be removed by providing the authentic ones.

Fake News

10.1109/confluence52989.2022.9734119

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Citations (3)

A Self-Adaptive and Self-Learning Methodology for Wireless Intrusion Detection using Deep Neural Network

Türk bilgisayar ve matematik eğitimi dergisi (2021)

R. Sathya

Cyber physical systems combine both the physical as well as the computation process. Embedded computers and systems monitor to control the physical forms with feedback loops which have an effect on computations and contrariwise. A vast number of failures and cyber-attacks are present in the cyber physical systems, which leads to a limited growth and accuracy in the intrusion detection system and thus implementing the suitable actions which may be taken to reduce the damage to the system. As Cyber-physical systems square measure but to be made public universally, the applying of the instruction detection mechanism remains open presently. As a result, the inconvenience is made to talk about the way to suitably apply the interruption location component to Cyber physical frameworks amid this paper. By analysing the unmistakable properties of Cyber-physical frameworks, it extraordinary to diagram the exact necessities 1st. At that point, the arranging characterize of the intrusion discovery component in Cyber-physical frameworks is introduced in terms of the layers of framework and particular location procedures. At long last, a few imperative investigation issues unit known for edifying the following considers.

Cyber-physical system

Component (thermodynamics)

Physical system

10.17762/turcomat.v12i6.4813

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Production of milk clotting protease by a local isolate of Mucor circinelloides under SSF using agro-industrial wastes

Biotechnology and Bioprocess Engineering (2009)

R. Sathya B. V. Pradeep Jayaraman Angayarkanni M. Palaniswamy

Husk

Mucor circinelloides

Yeast extract

Rennet

10.1007/s12257-008-0304-0

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Citations (49)

Image analysis-based discoloration rate quantification and kinetic modeling for shelf-life prediction in herb-coated pear slices

Scientific Reports (2024)

R. Sathya Prasad Rasane Aishvina Singh Jyoti Singh Sawinder Kaur

Abstract The present research study aimed to examine three different herb extract's effects on the discoloration rate of fresh-cut pear slices using an image analysis technique. Pear slices were sprayed and dip-coated with Ocimum basilicum, Origanum vulgare, and Camellia sinensis (0.1 g/ml) extract solution. During 15 days storage period with three days intervals, all sprayed/dip-coated pear slices were analyzed for the quality attribute (TA) and color parameters notably a*, b*, hue angle (H*), lightness (L*), and total color change (ΔE). Further, order kinetic models were used to observe the color changes and to predict the shelf-life. The results obtained showed that the applicability of image analysis helped to predict the discoloration rate, and it was better fitted to the first-order (FO) kinetic model (R 2 ranging from 0.87 to 0.99). Based on the kinetic model, color features ΔE and L* was used to predict the shelf-life as they had high regression coefficient values. Thus, the findings obtained from the kinetic study demonstrated Camellia sinensis (assamica) extract spray-coated pear slices reported approximately 28.63- and 27.95-days shelf-stability without much discoloration compared with all other types of surface coating.

Hue

Lightness

10.1038/s41598-024-51840-y

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Isolation and screening of glucose isomerase producing marine Streptomyces species for fructose production

Der pharma chemica (2014)

R. Sathya T. Ushadevi

The present study is to isolate and screening of glucose isomerase from marine Streptomyces species for industrial application of fructose. Soil samples were collected from Muthupet mangroves, Tamilnadu, India. Marine Streptomyces has emerged as an in exhaustive treasure for a wide range of enzymes like glucose isomerase. Glucose isomerase screening was performed by three different methods such as plate assay using xylose containing peptone yeast agar medium, fructose estimation by seliwanoff’s test and isomerization of glucose to fructose was detected by thin layer chromatography. Streptomyces species were grown on peptone yeast extract broth, the cell biomass was harvested then disrupted by ultrasonic disintegrator and centrifuged; cell free extracts were used for glucose isomerase activity. Thin layer chromatography separation provides a good result for isomerization of glucose to fructose by glucose isomerase. This finding proved numerous Streptomyces species has been produced glucose isomerase activity, particularly Streptomyces sp. RSU26 has been proved superior performing for glucose isomerase production and can be used for industrial application of fructose production.

Glucose-6-phosphate isomerase

Xylose isomerase

Yeast extract

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Underwater image enhancement by dark channel prior

2015 2nd International Conference on Electronics and Communication Systems (ICECS) (2015)

R. Sathya M. Bharathi G. Dhivyasri

Light scattering and color change are two main problems in underwater images. Due to light scattering, incident light gets reflected and deflected multiple times by particles present in the water. This degrades the visibility and contrast of the underwater image. Dark channel prior is method used for removing the haze present in the underwater image. It is based on a key observation - most local patches in haze-free underwater images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging color model estimates the thickness of the haze and recover a high quality haze-free image. This method does not require images with different exposure values, and is entirely based on the attenuation experienced by point across multiple frames. In this paper underwater image enhancement using Dark channel Prior is attempted. Results shows that the performance of this method is better compared to image enhancement using histogram equalization.

Haze

Visibility

Gamma correction

Color correction

10.1109/ecs.2015.7124757

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Citations (41)

Survey on Underwater Image Enhancement

CiiT international journal of digital image processing (2015)

R. Sathya M. Bharathi

Visibility

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Covid Wave Prediction using SARIMA Machine Learning Algorithm

2020 IEEE International Students' Conference on Electrical,Electronics and Computer Science (SCEECS) (2023)

R. Sathya Leander P Steve N. Lakshmi Narayan Abhishek Upadhye M. Aravindharaj

A speedy and accurate diagnosis of COVID-19 is made possible by effective SARS-Co V -2 screening, which can also lessen the strain on health care systems. There have been built prediction models that assess the likelihood of infection by combining a number of parameters. These are intended to help medical professionals worldwide prioritize patients, particularly when there are few healthcare resources available. From a dataset of 51,831 tested people, out of which 4,769 were confirmed to have COVID-19 virus, a machine learning method was developed and trained. Records of the following week with 47,401 tested people, of which 3,624 were tested positive was also considered. Our method accurately predicted the COVID-19 test results using eight binary characteristics, including gender, age 60, known contact with an infected person, and the presence of five early clinical signs.

Binary classification

10.1109/sceecs57921.2023.10062979

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