Informatics Publishing Limited

M. Ramkumar ¹, P. Sudha Rani ¹, M. S. Gandhi ¹, M. Pattabhi Ramayya ¹, V. Rajani Kumari ¹, K. V. S. Bhagavan ¹, A. S. R. Swamy ¹

Affiliations
1 Delta Studies Institute, Andhra University, Visakhapatnam - 530 003, IN

Abstract

Textural data of the sediments of Godavari delta indicate the control of energy over grain size and other statistical parameters. These parameters also explain the regional energy conditions, which are indicative of many sub-environments. The prime environmental factors viz. transportation, deposition and preservation vary in time and space, which in turn, produce variability as well as shared nature of grain size characteristics. Many sub-environments have similar depositional/energy regimes making their distinction a difficult task. It is also established that only groups of environments could be delineated, rather than individual environments, based on manual observations and discriminant plots of textural data.

Keywords

Texture, Grain Size, Statistics, Sedimentary Environments, Godavari Delta.

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M. Ramkumar ¹

Affiliations
1 Delta Studies Institute, Andhra University, Visakhapatnam - 530 003, IN

Abstract

Recent mapping of the Kakinada spit and the adjoining regions has revealed that the spit has reached its mature state and would not grow lengthwise. Another spit paralleling the Kakinada spit is growing to its east. Futuristic projections show that the new spit would form another bay like the Kakinada bay.

Keywords

Deltaic Landforms, Sedimentation, Recent Changes, Sea Level, Godavari Delta, Andhra Pradesh.

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M. Ramkumar ¹, M. Pattabhi Ramayya ¹

Affiliations
1 Delta Studies Institute, Andhra University, Visakhapatnam - 530 003, IN

Abstract

A simple, manually operated water sampler, that can be used for collection of water sample at desired depths from estuaries, near shore regions, lagoons, bays and lakes has been designed by the authors. The instrument is operated successfully in the Krishna estuary and Nizampatnam bay for collection of water samples to determine suspended sediment transport and nutrient diffusion. The instrument is found to be quite useful to sedimentologists and environmental researchers.

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M. Ramkumar ¹

Affiliations
1 Humboldt Environmental Research Organisation, D-371, Gandhiadigal Street, Annanagar, Tiruchirapalh - 620017, IN

Abstract

Sedimentary micro-environmental characteristics, landforms and morphostrati graphic details of the Godavari delta reveal that the progradational trends of the delta are favoured by channel bifurcation and lengthening in upper deltaic plain. In the lower deltaic regime, formation of barrier bar followed by lagoon and lagoon infilling lead to deltaic progradation over sea. The progradational process was such that, initially, the delta front deposits developed low gradients in continental shelf over which spit and distributary mouth bars were deposited that paved way for the development of lagoonal conditions. It was followed by creation of swamps and marshes that in turn were overlapped finally by flood plain as a complete sequence of delta progradation. The neotectonic activity has played a pivotal role in this progradational process by tilting fault blocks that provided adequate gradient and alternative activation of various distributaries of the river. On the basis of facies architectural elements, sedimentary processes and the pattern of sedimentation, the Godavari delta can be considered as modern analog of abandoned Lafourche delta of Mississippi delta complex. Analyses of shoreline features, ongoing sedimentary pattern and the role of depositional and erosional agents over different sedimentary environments indicate that the delta currently engages erosional phase in view of rising sea level coupled with subsidence and is not prograding over sea as happened during its evolutionary history.

Keywords

Deltaic Environments, Landforms, Neotectomcs, Sea Level Godavari Delta.

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M. Ramkumar ¹

Affiliations
1 Delta Studies Institute, Andhra University, Visakhapatnam - 530 003, IN

Abstract

Documentation of the textural and geochemical criteria of individual sub-environments of the modern Godavari delta shows that in view of prevalent shared nature of transportation, deposition and preservation processes, the textural and geochemical characteristics are not distinct enough to have these sub-environments separated by visual methods on the basis of measurements of constituent variables. Multivariate discriminant function analyses of these data show that the sub-environments of the modern deltaic system could be separated practically to the tune of 80.26% accuracy. The percentage of distinctness depends on the sensitivity of each parameter to different sub-environments and also the ability to separate between sample variations. These results have enabled the construction of a scheme for recognition of environments of unknown samples.

Keywords

Granulometry, Geochemistry, Sedimentary Environments, Statistical Discrimination.

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M. Ramkumar ¹, M. Babu ², R. Lakshminarayanan ¹

Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Sciences, IN

Abstract

Nowadays, processing the medical image is a most significant diagnostic process. Usually RMI is used to detect the presence of and type of tumor. The following process is very complicated in the brain tumor classification. The treatment of medical images, such as image segmentation, image extraction, and image classification, takes various steps. Various types of properties such as intensity, forms and texture-based features are extracted from a segmented MRI image. The feature selection approach is employed to select a small subset of MRI image features that minimize redundancy and maximize target-related pertinence. This article uses the Bees Swarm Optimization (BSO) for the selection and the Neural Network Classifier to classify the type of tumor in present brain MRI images, and then takes online MRI images which contain brain tumor, then a machine-learning model.

Keywords

Neural Network, ACO, Feature Extraction, Classification, MRI.

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M. Babu ¹, M. Ramkumar ¹, M. Shenbagapriya ²

Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Electronics and Communication Engineering, Sri Satya Sai University of Technology, IN

Abstract

Wireless Sensor Network (WSN) can be used as an important concept to reduce the redundancy and energy consumption. To optimize the wireless sensor networks for secured data transmission both at cluster head and base station, data aggregation is needed. The existence time of sensor network diminishes due to energy inefficient nodes for data aggregation. Henceforth aggregation process in WSN ought to be advanced in energy efficient way. Data aggregation is performed in every router while forwarding data. It is difficult to identify and isolate the compromised nodes so as to abstain from being deceived by the distorted data infused by the enemy through compromised nodes. In any case, it is trying to secure the flat topology network effectively in light of the poor adaptability and high communication overhead. We discuss a mechanism that distinguishes malicious nodes by the collaboration of appropriate nodes and logically isolates the recognized, malicious nodes from remote sensor systems. Also this paper describes about the attacks and security goals in the WSN.

Keywords

Wireless Sensor Network, Data Aggregation, Malicious Node, Security Goals.

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M. Ramkumar ¹, R. Manikandan ², M. Punithavalli ³, V. S. Akshaya4 ⁴, Shanmugaraj Madasamy ⁵

Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science, The Quaide Milleth College for Men, IN
3 Department of Mechanical Engineering, Rathinam Technical Campus, IN
4 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, IN
5 Department of Computer Science, Cork Institute of Technology, IE

Abstract

Cervical cancer is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with cervical cancer. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses Convolutional Neural Network (CNN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a pre-processing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over cervical cancer image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models.

Keywords

Machine Learning, Cervical Cancer, Classification, Diagnosis.

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M. Ramkumar ¹, J. Gowrishankar ², V. Amirtha Preeya ³, T. Pushpa ¹, T. Karthikeyan ⁴

Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Jain University, IN
3 Department of Computer Science and Engineering, Presidency University, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM

Abstract

The current study employs biclustering to alleviate some of the drawbacks associated with gene expression data grouping. Different biclustering algorithms are used in this study to detect unique gene activity in various contexts and reduce the duplication of broad gene information. Furthermore, machine learning or heuristic algorithms have become widely utilised for biclustering due to their suitability in problems where populations of potential solutions allow examination of a larger percentage of the research area. To begin with, gene expression data biclusters frequently contain data that is the same under a variety of different situations of gene expression. Therefore, the biclustering technique is particularly effective if the matrix lines and columns are merged immediately. Submatrices can be identified using the Large Average Sub matrix. A Fuzzy C-Means algorithm is also used to ensure that the sub-matrix can be expanded to include more rows and columns for further analysis. The sub-matrices and component precision and strength are factored into the system design. It uses biclustering techniques to differentiate gene expression information. On the Garber dataset, the simulation is run in Java. Using the average match score for non-overlapping modules, the influence of noise on overlapping modules using constant bicluster and additive bicluster, and the overall run duration, the study is assessed.

Keywords

Heuristic Algorithm, Gene Expression, Data Biclusters, Fuzzy C-Means

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M. Ramkumar ¹, Amirtha Preeya ², R. Manikandan ³, T. Karthikeyan ⁴

Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Presidency University, IN
3 Department of Computer Science, The Quaide Milleth College for Men, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM

Abstract

In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods.

Keywords

Iris Detection, Pattern Identification, Liveness Detection, Biometric, Deep Learning

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J. Logeshwaran ¹, M. Ramkumar ², T. Kiruthiga ³, Sharan Pravin Ravi ⁴

Affiliations
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, IN
2 Department of Computer Science and Engineering, HKBK College of Engineering, IN
3 Department of Electronics and Communication Engineering, Vetri Vinayaha College of Engineering and Technology, IN
4 Department of Automation Control and Robotics, Sheffield Hallam University, GB

Abstract

In modern companies, the functions of divisions, departments and staff are provided by telecommunication transmitting analog and digital unit information via SCS. Such cable system refers to the use of copper or optical cable networks, passive and active switching devices. Structured cabling system or abbreviated SCS is a complex set of cable trunks and switching equipment that provide the transfer of various types of media data (audio, video, computer data) and is the basis for the operation and integration of telephone, local computer networks, security systems and other services. Many modern systems of security or communications today integrate a wide variety of interfaces into their arsenal, greatly expanding their capabilities and performance. In this paper a smart model based on high-speed communication network with the help of structured cabling system (SCS). Here the speed and bandwidth play the major role. The proposed system focused the highspeed communication between sender and receiver with some higher bandwidth optimization.

Keywords

Optical Cable Network, Switching Device, Structured Cabling System, Communication Network, Security System

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M. Ramkumar ¹, M. Jasmine ²

Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Horticulture, School of Agriculture, Ponnaiyah Ramajayam Institute of Science and Technology, IN

Abstract

In this paper, we provide the data of supply chain management of agriculture product namely mango fruit. The study shows how well the product is undergoing supply chain management from field to user via various chains. The study presents how well the supply chain management makes an effective delivery of product with series of chains in supply chain management. The study also takes into concern the activities that coordinates with the enterprises to deliver the product to customer by meeting various demands like quality, quantity and cost. The study takes into concern the deepest interactions of all the levels of supply chain in delivering the product to the end user.

Keywords

Supply Chain Management, Agriculture Product, Mango Fruit.

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M. Ramkumar ¹, V. Amirtha Preeya ², V. Sathiyapriya ³, M. Marimuthu ⁴

Affiliations
1 Department of Computer Science and Business Systems, Knowledge Institute of Technology, IN
2 Department of Computer Science Engineering, Presidency University, IN
3 Department of Computer Science and Engineering, Knowledge Institute of Technology, IN
4 Department of Computer Science and Engineering, Sona College of Technology, IN

Abstract

Many strains of corona virus such as alpha, beta, gamma, delta, and omicron are still prevalent in various parts of the world. The new type of corona virus is called a variant when it is caused by more than one genetic mutation from the previous type of corona virus. Various strains around the world have come so far. The cough may persist for more than an hour or three or four times in 24 hours and body heat is high. You may not be able to feel the smell or taste. Researchers say that some people may have symptoms similar to those of a severe cold. In this paper, a multi threshold model was proposed to find the initial infection detection of COVID disease. Based on the initial health symptoms these methods observe the inputs of the patients. Then the observed symptoms are compared with the existing database and identify the spreading of the disease. This report was directly monitored by the patient and doctor. This model was helpful to provide the periodical monitoring and perfect treatments to the infected patients.Many strains of corona virus such as alpha, beta, gamma, delta, and omicron are still prevalent in various parts of the world. The new type of corona virus is called a variant when it is caused by more than one genetic mutation from the previous type of corona virus. Various strains around the world have come so far. The cough may persist for more than an hour or three or four times in 24 hours and body heat is high. You may not be able to feel the smell or taste. Researchers say that some people may have symptoms similar to those of a severe cold. In this paper, a multi threshold model was proposed to find the initial infection detection of COVID disease. Based on the initial health symptoms these methods observe the inputs of the patients. Then the observed symptoms are compared with the existing database and identify the spreading of the disease. This report was directly monitored by the patient and doctor. This model was helpful to provide the periodical monitoring and perfect treatments to the infected patients.

Keywords

Alpha, Beta, Gamma, Delta, Omicron, COVID, Threshold Model.

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