S. Pazhanirajan

Annamalai University

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Nagarjun Yadav Vanguri

Annamalai University

P. Dhanalakshmi

Annamalai University

Kiran Maka

Annamalai University

T. Anil Kumar

Advanced Numerical Research and Analysis Group

R Kumar

Annamalai University

Kiran Maka

Sujata Mallapur

Annamalai University

Lakshmi Maka

Anil Kumar T.

Advanced Numerical Research and Analysis Group

R. Kumar

Punjab Agricultural University

Cooperative Institutions

Annamalai University

Advanced Numerical Research and Analysis Group

Visvesvaraya Technological University

SRM Institute of Science and Technology

Bharathiar University

Aeronautical Development Agency

Yogi Vemana University

Jawaharlal Nehru Technological University Anantapur

Indian Institute of Technology Delhi

Punjab Agricultural University

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Oropharyngeal Cancer Prediction and Optimization Using Improved VGG16 with Grey Wolf Optimizer

International Journal of Electronics and Communication Engineering (2024)

R. Kumar S. Pazhanirajan

Oropharyngeal cancer, a subtype of head and neck cancer, presents significant challenges in early detection and treatment. In this paper, we propose a novel approach for predicting and optimizing oropharyngeal cancer using an improved VGG16 architecture with the Grey Wolf Optimizer (GWO) algorithm. The utilization of deep learning techniques has shown promise in medical image analysis, particularly in cancer detection, due to their ability to extract complex features from imaging data. The VGG16 architecture, known for its deep convolutional layers, is enhanced with additional layers and regularization techniques to improve its predictive performance. We utilize a comprehensive dataset comprising high-resolution medical images of oropharyngeal tissues for training and evaluation purposes. The dataset is preprocessed to enhance image quality and standardize features across samples. Subsequently, the proposed model is trained using this dataset, with the objective of accurately classifying images as either cancerous or non-cancerous. Experimental results demonstrate the effectiveness of the proposed approach in predicting oropharyngeal cancer with high accuracy and efficiency. Compared to traditional methods, the improved VGG16 architecture combined with the Grey Wolf Optimizer achieves superior performance in terms of both prediction accuracy and convergence speed. Moreover, the model exhibits robustness against variations in imaging conditions and patient demographics.

10.14445/23488549/ijece-v11i9p111

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DLSTMFRNN a newly developed network-based deep long short-term memory and recurrent neural network for stock market prediction

International Journal of Web Engineering and Technology (2024)

Anil Kumar T.S. Pazhanirajan Nagarjun Yadav Vanguri

Stock Market Prediction

10.1504/ijwet.2024.10063128

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WITHDRAWN: Selection of most significant variables to detect fraud in financial statements

Materials Today Proceedings (2020)

Kiran Maka S. Pazhanirajan Sujata Mallapur

In this work, a methodology is proposed to detect fraud in financial statements. The financial statements of Indian companies were used as the dataset. The models were trained on undersampled and oversampled datasets. A total of 38 models were trained on undersampled and oversampled datasets. Top metrics like accuracy, sensitivity and precision were used to select the top performing models in prediction of fraud in test dataset. The methodology to select the top performing models is explained in detail. The most significant features from the top performing models are extracted and common features are selected. The common features can be used to detect the fraud in financial statements quickly in practice.

Financial fraud

10.1016/j.matpr.2020.09.613

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AN ACADEMIC REVIEW OF DATA MINING TECHNIQUES IN FRAUD DETECTION

Journal of Critical Reviews (2020)

Kiran Maka S. Pazhanirajan Sujata V. Mallapur

Economically, financial fraud is becoming an increasingly serious problem. In recent years, financial fraud, including credit card fraud, corporate fraud and money laundering, has attracted a great deal of concern and attention. In order to provide solution to financial fraud this paper reviews various Data Mining Techniques in Financial fraud Detection such as Ada boost algorithm, decision trees, Bayesian Belief Network, Neural networks, discriminant analysis , K-nearest neighbor, logistic model, discriminant analysis, Naive Bayes, neural networks, decision trees , Support vector machine, evolutionary algorithms to find the best possible technique.

Credit card fraud

Source

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Citations (1)

MRI Classification of Parkinson’s Disease Using SVM and Texture Features

Advances in intelligent systems and computing (2015)

S. Pazhanirajan P. Dhanalakshmi

Gray level

10.1007/978-81-322-2523-2_34

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Citations (2)

Tversky-RideNN based Feature Fusion and Optimized Deep RNN for Stock Market Prediction

2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA) (2022)

Nagarjun Yadav Vanguri S. Pazhanirajan T. Anil Kumar

The stock market prediction has been considered a significant time-series forecasting research domain. The accurate prediction of stock prices helps the investors to gain more profits and to make better decisions while selling and buying the stock products. However., due to stock data's inherent., dynamic., and non-linear characteristics., the prediction process is more complex and remains a challenging task. Hence., this research proposes an effective strategy for accurate stock market prediction using the newly proposed Adaptive Competitive Feedback Particle Swarm optimization (CFPSO) method. The proposed approach includes four steps: extraction of technical indicators., feature fusion., data augmentation., and stock market forecasting. The stock market prediction mechanism is done using Deep Recurrent Neural Network (Deep RNN)., where the network is trained using Adaptive CFPSO. Moreover., the proposed approach has achieved a minimum MAE of 0.113., minimum MSE of 0.095., and minimum RMSE of 0.213.

Stock (firearms)

10.1109/icirca54612.2022.9985572

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Citations (2)

EEG Signal Classification using Linear Predictive Cepstral Coefficient Features

International Journal of Computer Applications (2013)

S. Pazhanirajan P. Dhanalakshmi

An electroencephalogram (EEG) is a procedure that records brain wave patterns, which are used to identify abnormalities related to the electrical activities of the brain.In this study an effective algorithm is proposed to automatically classify EEG clips into two different classes: normal and abnormal.For categorizing the EEG data, feature extraction techniques such as linear predictive coefficients (LPC) and linear predictive cepstral coefficients (LPCC) are used.Support vector machines (SVM) is used to classify the EEG clip into their respective classes by learning from training data.

SIGNAL (programming language)

Linear prediction

Cepstrum

Mel-frequency cepstrum

10.5120/12707-9508

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Citations (13)

Deep Learning Technique for Oropharyngeal Cancer Detection in Medical Imaging

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (2023)

R Kumar S. Pazhanirajan

Oropharyngeal carcinoma is a life-threatening disease that requires prompt diagnosis and treatment. This study uses deep learning algorithms to present a novel method for improving medical imaging to detect oropharyngeal cancer. Deep learning has demonstrated immense potential for medical image processing applications in recent years. This research capitalizes on this opportunity by devising a dependable model for detecting oropharyngeal cancer. Examining oropharyngeal cancer with an emphasis on the critical significance of early detection. This study emphasizes the need for more precise and efficient diagnostic techniques. This work presents a comprehensive strategy from data acquisition and cleansing to the development of the model's underlying architecture, training, validation, and explication. The results section presents quantitative and qualitative data demonstrating the model's ability to detect oropharyngeal cancer. The proposed model has the potential to improve the quickness and accuracy with which oropharyngeal cancer is diagnosed, thereby benefiting patients. It emphasizes the need for additional research and the implementation of deep learning innovations in healthcare.

Cancer Detection

10.1109/iceca58529.2023.10395576

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Establishing Causality to Detect Fraud in Financial Statements

International Journal of Circuits Systems and Signal Processing (2021)

Kiran Maka S. Pazhanirajan Sujata Mallapur

In this work, two approaches have been presented to derive the important variables that an auditor should watch out for during the audit trials of a financial statement. To achieve this goal, machine learning modeling is leveraged. In the first approach, important features or variables are derived based on ensemble method and in the second approach, an explainable model is used to corroborate and expand the conclusions derived from the ensemble method. A dataset of financial statements that was labeled manually is utilized for this purpose. Four important measures, namely, random forest recommendations of first approach, random Forest Explaner -pvalue, random Forest Explainer-first multi-way importance plot and random Forest Explainer-second multi-way importance plot, are employed to derive the important features. A final list of six variables is derived from these two approaches and four measures

Statement (logic)

Financial statement

Causality

Plot (graphics)

10.46300/9106.2021.15.166

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Competitive feedback particle swarm optimization enabled deep recurrent neural network with technical indicators for forecasting stock trends

International Journal of Intelligent Robotics and Applications (2022)

Nagarjun Yadav Vanguri S. Pazhanirajan T. Anil Kumar

10.1007/s41315-022-00250-2

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Citations (7)