Department of Information Technology

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Start date: 2020End date: 2023

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Now showing 1 - 9 of 9
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    IMPACT OF ACINETOBACTER BAUMANNII ON DYE DEGRADATION AND A MOLECULAR ANALYSIS STUDY
    (Springer Link, 2023-06-05) Nivetha V; Harini S; Maria Shyla J; Sophia Reena G
    Acinetobacter baumanni was isolated from polluted soil. An attempt to study the A. baumannii to degrade dye was explored. It was found effective against azo dye and was able to completely degrade the dye under 48 h in a shake flask. Molecular analysis on the isolate A. baumanni was performed together with Hedychium flavum, and the sequence was submitted to the NCBI database to procure accession number MT192652.1. Response surface Methodology-Box-Behnken design (RSM-BBD) was used to optimize the condition and achieve 98–99% dye decolorization.
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    EMERGING METHODOLOGIES FOR THE MOLECULAR ANALYSIS OF SOIL MICROBIOTA FROM POLLUTED SOIL SITES
    (Springer Link, 2023-06-05) Ridhuvarshini, Pavethra; Sophia, Reena; Sivaranjani
    The soil microbiome performs a wide range of crucial functions; however, we have a limited understanding of its biodiversity. Extracting microbes from polluted sites could reveal potential microbes that could be used to mitigate pollution better than conventional microbes. Soil DNA may be extracted directly, amplified using polymerase chain reaction, and profiled to reveal more about the soil microbiome’s taxonomy and function than ever before. Current procedures frequently combine DNA sequencing with other methods like denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), single-strand conformation polymorphism (SSCP), terminal restriction fragment length polymorphism (TRFLP), amplified rDNA restriction analysis (ARDRA), amplified ribosomal intergenic spacer analysis (ARISA), and cloning. The advantages and disadvantages of these methods are discussed, and new developments that have relevance as an appliance shedding light on the soil microbial ecology are also included. Soil diversity cannot be assessed using just one approach; therefore, picking the right one and using newly discovered information can significantly improve our understanding of soil microbes for their specific applications in mitigating.
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    ASSOCIATION RULE MINING FOR CLIQUE PERCOLATION ON COMMUNITY DETECTION
    (SERSC, 2020-01) Sathiyakumari K; Vijaya M.S
    The recognition of communities linking like nodes is a demanding subject in the revision of social network data. It has been extensively considered in the social networking community in the perspective of underlying graph structure besides communication among nodes to progress the eminence of the discovered communities. A new approach is proposed based on frequent patterns and the actions of users on networks for community detection. This research work spends association rule mining to discover communities of similar users based on their interests and activities. The Clique Percolation technique initially anticipated for directed networks for driving communities is enlarged by using the ascertained prototypes for seeking network components, i.e., internally tightly linked groups of nodes in directed networks discovering overlapping communities efficiently. The community measures such as the bulk of the community, piece of community and modularity of the community are used for testing the reality of communities. It tests the proposed community detection approach using a sample twitter data of sports person networks with F-measure and precision showing that the proposed method principals to improve the community detection quality.
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    DATA MINING IN FRAUD DETECTION
    (Sri Krishna Arts and Science College, 2020-02-12) Hashni T; Harini S; Janani Shree G; Divya K
    Fraud is an increasing crime in day-to-day modern world. Fraud possibilities co-evolve withtechnology especially with InformationTechnology. Fraud detection is amethod/technique of identifying illegal actswhich are offensive, that are occurring all aroundthe world. It defines a skilled impostorformulizes the key forms and sub forms ofrecognized frauds and reveals the gathered datanature. To detect the fraud patterns from datacollected/stored, the paper explains somepreferred data mining techniques. Data mining ismost commonly used for fraud detection andprevention among various tools available. Thispaper gives an idea in a well-defined way by which any number of frauds can be detected and analyzed. This paper also describes clearly aboutdifferent types of fraud detection techniques.Theme of this paper is to firstly identify the typeof fraud using data mining techniques and toresolve the criminal aspect in simplified way.
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    WIRELESS SENSOR NETWORK IMPACT ON HUMAN LIFE
    (Multi-Disciplinary Application Perspective in IoT”Vol. 68 No. 19 (2020) @Our Heritage Journal, 2020-01-19) Hashni T; Madhumitha D; Indrakshi S
    Wireless Sensor Network(WSN) refers to a group of spatially dispersed and dedicated sensors formonitoring and recording the physical conditions of the environment and organizing thecollected data at a central location . This paper briefly explains about wireless networks impactin the life of human.
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    BLOCK CHAIN WITH IOT
    (Our Heritage Journal, 2020-01-23) Sivaranjani B; Kanishka C; Mahesh J
    This paper is regarding Block chain, IOT and the block chain with IOT. Internet of Things plays a major role in the present technological world, but to the point of security and privacy IOT lacks in it. To overcome this Block chain concept arrives. The combination of IOT devices and Blockchain introduces the purest source of IOT data. Blockchain enables IOT’s unique identity, accuracy and consistency of data. Blockchain and IOT both tend to be the world changing technology.
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    WIRELESS SENSOR NETWORK IN AGRICULTURE
    (Michael Job College Of Arts and Science For Women, 2020-02-19) Nithya S; Yuvashree R; Sowmiya P
    Wireless network sensor(WSN) is a self configurable to monitor physical or environment condition. It contain many node is equipped with sensing and computing devices. In this paper we discussed about how WSN used in agriculture it employ as a part of agriculture for many reasons. India is 2nd in agriculture activities. The agriculture production process is affected by different factor such as temperature ,light, soil moisture . It provide accurate information about environmental condition to formers. It helps to increase the production of crops , low power consumption and gather distributed data. The wireless network technologies are increasingly being implemented for modern precision agriculture monitoring. WSN is also used in soil and water condition management .
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    ARTIFICIAL INTELLIGENCE (AI) IN AGRICULTURE
    (Our Heritage, 2020-01-19) Nithya S; Nivetha V; Varshini S
    Agriculture and farming is one of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various technologies. As the world population continues to grow and land becomes scarcer, people have needed to get creative and become more efficient. AI is most common in growing sectors, now AI is breaking into agriculture sector too (Fig .1). Agriculture plays a huge role in developing our economy but it has certain arising problems like lack of labours, water scarcity etc, the solution for this is use of artificial intelligent agriculture. AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone. In addition to ground data, farmers are also taking to the sky to monitor the farm.
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    PANCREATIC TUMOR SEGMENTATION IN RECENT MEDICAL IMAGING – AN OVERVIEW
    (“Advances in Intelligent Systems and computing” 2194-5357 @ Springer Nature Switzerland AG 2020 S. pp.514 -522, 2020-01-07) Sindhu A; Radha V
    Pancreatic tumor is one of the deadliest diseases, which is the fourth leading cause of cancer death worldwide. Detecting pancreatic cancer at an early stage may increase the life of the patients. Pancreatic tumor segmentation is one of the difficult challenges in medical field. Accurate and Efficient segmentation in medical images are emerging as a challenging task during radiotherapy planning. Various medical modalities like MRI, CT and PET are widely used for diagnosing the abnormalities present in the medical images. Image segmentation plays an important part for the exact detection of the tumor in diagnosing, detecting, treatment and planning. In this review paper, various algorithms are used for segmenting the pancreatic tumor in medical images were discussed.