D. Yuvaraj

Cihan University-Erbil

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M. Sivaram

Saveetha University

Mohamed Uvaze Ahamed Ayoobkhan

National University of Uzbekistan

B. Bazeer Ahamed

University of Technology and Applied Sciences - Al Mussanah

V. Porkodi

Saveetha University

Subbiah Swaminathan

Rajalakshmi Engineering College

S. Shanmuga Priya

Vels University

Shanmugasundaram Hariharan

Synergy University

P. Sundaravadivel

Saveetha University

V. Manikandan

Jain University

Pradeep Sudhakaran

SRM Institute of Science and Technology

Cooperative Institutions

Anna University, Chennai

Vellore Institute of Technology University

SRM Institute of Science and Technology

Saveetha University

SASTRA University

Karpagam Academy of Higher Education

Annamalai University

Sathyabama Institute of Science and Technology

National Institute of Technology Tiruchirappalli

KPR Institute of Engineering and Technology

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WITHDRAWN: Hybrid approach for melonama detection in dermoscopic images

Materials Today Proceedings (2021)

D. Raju Shanmugasundaram Hariharan D. Yuvaraj

10.1016/j.matpr.2021.01.158

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Citations (4)

Hybrid Approach for Human Diseases Prediction Using Air Quality Index

ROMANIAN BIOTECHNOLOGICAL LETTERS (2022)

D. VENU D. Yuvaraj Mridhula Murali Nitika Vats Doohan

Air pollution has become an extremely serious issue as the air pollutants emitted from motor vehicles has a greater impact on human health than other contaminants. Air quality forecasting plays a major role in giving warning to people and controlling air pollution. The single technique forecasting has various drawbacks such as low accuracy, low performance and low speed. Our present work overcome the above drawbacks by using a hybrid model approach. Our proposed method aims to forecast air quality to predict the hourly concentration of air pollutants using a hybrid model of data mining and machine learning. It predicts diseases due to emission of air pollutants from the motor vehicles based on Air Quality Index level. The CLusteringInQUEst algorithm is used to cluster geo-spatial data for specific input region. The Air Quality Index (AQI) for desirable set of mportant air pollutant features was calculated from the datasets produced by air pollutants from atmosphere. The calculated AQI was the input to the eXtreme Gradient Boosting (XGB) decision tree. It then classifies AQI level for the specific air pollutants. Then the diseases were classified using XGB algorithm.CLIQUE method has chosen than any other data mining techniques for which it can accurately predict diseases based on AQI values. XGBoost classifier is known for its good performance gradient bosting tree models which is very fast and an efficient one for both computation time and memory. Hence the above two techniques were combined as a hybrid approach to get the benefits of those features.The hybrid model produces a result with a higher performance, accuracy and speed compared to other models. In this paper, we have compared accuracy and precision rates for the hybrid approach with two single techniques such as Support Vector Machine and Random Forest.An accuracy and Precision rates of our proposed hybrid approach was 98.6% and 98.7% than Support Vector Machine has 93.85% and 94.8% & Random Forest has 94.28% and 94.52% which proves that hybrid approach is an efficient diseases prediction technique in real-time environment.

Air pollutant concentrations

Criteria air contaminants

Gradient boosting

10.25083/rbl/27.1/3270-3281

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Citations (1)

Decision Support System for Determining Academic Advisor Using Simple Additive Weighting

Advances in intelligent systems and computing (2020)

M. Sivaram S. Shanmugapriya D. Yuvaraj V. Porkodi Ahmad Akbari

Supervisor

Academic community

Continuation

Academic institution

Learning development

10.1007/978-981-15-1451-7_16

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Citations (13)

Medical Image Segmentation of Bio-medical Images with Deep Convolution Neural Networks using Ensemble Approach

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) (2022)

D. Yuvaraj A K Sampath N. Shanmugapriya Badugu Samatha S. Arun

Image analysis relies heavily on segmentation, which encompasses detecting the features, extraction of features, classification, and treatment. For treatment planning, segmentation assists physicians in quantifying amount of tissue. The segmentation methods are divided into three groups: supervised, unsupervised, and DL segmentation, as well as traditional and computational modelling segmentation. Ensemble techniques in combine many learning models to achieve greater prediction results than each of the individual deep learning algorithms separately. In most scenarios, utilising a very simple ensemble resulted in a lower error rate than using a single model. This demonstrates the efficacy of ensemble approach. This research presents an ensemble deep learning model that was evaluated on ultrasound breast images of women between the ages of 25 and 75 using CNN, MaskR CNN, U Net, and Res Net. Since all four models operate with similar data shape, it makes sensible to design a single input layer that all models will use. The proposed model achieves the highest accuracy of 98.6% on the training, 94.5% validation accuracy, 94.5% test accuracy and a F1 score of 94.32% which outperforms all the individual neural networks.

Convolution (computer science)

Ensemble Learning

10.1109/icosec54921.2022.9952142

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Evaluation and Customized Support of Dynamic Query form through web search

G. Sakthi D. Yuvaraj M. Sivaram Mohamed Uvaze Ahamed Ayoobkhan

New sorted out web databases keep up enormous and assorted information and these genuine databases incorporate more than hundreds or even a colossal number of relations and characteristics. Normal predefined request structures are not prepared to satisfy distinctive uncommonly selected inquiries from customers on those databases. Dynamic Question Structure (DQF) is a novel database request structure interface, which can effectively make question shapes. The focal thought of DQF is to get a customer's tendency and rank request structure parts, profitable anyone to choose. The making of an inquiry structure is an iterative strategy and is guided by the customer. At each accentuation, the system subsequently makes situating courses of action of structure fragments and the customer by then incorporates the favoured structure parts into the request structure. The situating framework relies upon the got customer inclination. A customer can correspondingly stack the request structure and submit a request to see the inquiry result at each accentuation. Thusly, a request structure could be viably refined till the customer satisfies with the inquiry results. A probabilistic model is made for measuring the respectability of an inquiry structure in DQF. Exploratory appraisal and customer study demonstrate the sufficiency and adequacy of the structure are finished up with an experimentation results.

Information Structure

Rank (graph theory)

Interface (matter)

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Citations (4)

Deep attention based optimized Bi-LSTM for improving geospatial data ontology

Data & Knowledge Engineering (2022)

Palaniappan Sambandam D. Yuvaraj P. Padmakumari Subbiah Swaminathan

Boosting

10.1016/j.datak.2022.102123

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Citations (4)

Advanced Expert System Using Particle Swarm Optimization Based Adaptive Network Based Fuzzy Inference System to Diagnose the Physical Constitution of Human Body

Communications in computer and information science (2019)

M. Sivaram Amin Salih Mohammed D. Yuvaraj V. Porkodi V. Manikandan

Fuzzy inference system

10.1007/978-981-13-8300-7_29

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Citations (17)

Systematic Review of Security Authentication based on Block Chain

2022 3rd International Conference on Smart Electronics and Communication (ICOSEC) (2022)

D. Yuvaraj M Anitha Brijesh Singh Nagarjuna Karyemsetty R. Krishnamoorthy

One of the fifth generation's most promising solutions for addressing the network system capacity issue is the ultra-dense network. However, a new problem arises because the user equipment secure access is made up of access points that are independent, transitory, and dynamic. The APs are independent and equal in this. It is possible to think of it as a decentralized access network. The access point's coverage is less than the standard base stations. The user equipment will interface with access points more frequently as it moves, which is a problem. The current 4G Authentication and Key Agreement method, however, is unable to meet this need for quick and frequent authentication. This study means to research how blockchain innovation is being utilized in production network the executives, as well as its forthcoming purposes and arising patterns. To more readily comprehend the direction of important exploration and illuminate the benefits, issues, and difficulties in the blockchain-production network worldview, a writing overview and a logical evaluation of the current examination on blockchain-based supply chains were finished. Multifaceted verification strategies have as of late been utilized as possible guards against blockchain attacks. To further develop execution, scatter administration, and mechanize processes, inventory network tasks might be upset utilizing blockchain innovation

10.1109/icosec54921.2022.9952033

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Citations (6)

Spiking Equilibrium Convolutional Neural Network for Spatial Urban Ontology

Neural Processing Letters (2023)

Palaniappan Sambandam D. Yuvaraj P. Padmakumari Subbiah Swaminathan

Ontology learning

10.1007/s11063-023-11275-4

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Citations (3)

Use Case of Artificial Intelligence in Machine Learning Manufacturing 4.0

E. Balamurugan Laith R. Flaih D. Yuvaraj K. Sangeetha A. Jayanthiladevi

The sector manufacturing rebellion (Manufacturing generation) has been enticing consideration from entirel over the biosphere over the past few years. Digital technology progression has positioned companies in the face of a projected change in pari gms and management methods. Manufacturing 4.0 is altering the manufacturing sector. The novel scientific tendencies, the arrival of manufacturing 4.o and artificial Intelligence (AI) and machine-learning make it imperative to replicate on manufacturing practices and their efficiency with appreciate to the new technological framework. AI and Machine Learning in industry 4.0 paves way for immense revolution for manufacturing companies with different industry opportunities. Industry 4.0 embrace IoT, AI, cyber-physical systems, Machine learning and cognitive computing which enables the startups for smart factories. The production processes in smart factory are allied to interfaces, machines and modules communicate with one another where large amount of information can be collected to enhance the manufacturing process. Artificial intelligence and machine learning in manufacturing is focused on industrial automation in the pursuit of efficacy.

Manufacturing

Integrated Computer-Aided Manufacturing

Manufacturing execution system

Machine shop

10.1109/iccike47802.2019.9004327

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Citations (21)