AI technologies in autoimmune diseases: Advances, limitations and prospects S. Sasikala, M. Raghini, P. Alli AI Assisted Computational Approaches for Immunological Disorders, 2025 A groundbreaking advancement in healthcare is the application of AI algorithms to track patients' chronic diseases and vital signs, delivering real-time feedback to medical professionals. Monitoring and treating the symptoms of Oral Lichen Planus (OLP), a chronic inflammatory disorder affecting the mouth's mucous membranes, can be particularly challenging. The purpose of this systematic review is to disclose the most valid and reliable scoring systems suitable for clinical monitoring of disease progression and predicting response to therapy in Oral lichen planus patients.
AoDrA-Net: an approach to recommend crop for sustainable agriculture P. Prabharani, S. Appavu Alias Balamurugan, S. Sasikala International Journal of Bio Inspired Computation, 2024 Agriculture is a fundamental component of India's socio-economic structure. The inability of farmers to detect the most appropriate crop for the soil through conventional and non-scientific practices is a severe problem in a nation where 58% of the approximate population is engaged in agriculture. In some cases, farmers were unable to select the appropriate crops due to different soil conditions, different sowing seasons, and different regions. This results in suicide, abandoning agriculture, and shifting to metropolitan areas for livelihood. Archimedes optimised discrete deep residual AlexNet (AoDrA-Net) builds a complete crop recommendation framework (CRS-DDRAN-AOA) which offers direction and inspiration for the mentioned deficiencies. Initially, data is taken from the dataset of crop recommendation. Then the input data is pre-processed under z-score standardisation procedure. Then, the pre-processed output data is given to DDRAN augmented with AOA that accurately recommends the crop by lessening the error and raising the recommendation accuracy.
Detecting Gastro-Intestinal Cancer from Wireless Capsule Endoscopy Images using Efficient Net Model S. Geetha, V. Sharmila, S. Sasikala, S. Appavu Alias Balamurugan, N.M. Balamurugan 2023 14th International Conference on Information and Communication Technology and System Icts 2023, 2023 Recently polyps and ulcers have become a fatal form of gastrointestinal problem, and several researchers have looked into algorithms to diagnose gastrointestinal cancer lesions. This research work investigates the application of CNN to detect Gastro-Intestinal (GI) cancer from wireless capsule endoscopy (WCE) GI images. Five distinct CNN architectures EfficientNet, LeNet, GoogleNet, MobileNet-V2, and ResNet-50 have been studied, with two different setups i) original data with transfer learning ii) original data with data augmentation, and transfer learning. Simulations ran on 37,790 images with five different CNN architectures proved that the EfficientNet model exhibited superior performance of about 99.15% accuracy over all other CNN models, on the augmented dataset. Furthermore, the results from the experiments show that the EfficientNet model significantly outperforms previous methods in terms of classification accuracy.
Enhanced bilstm model for eeg emotional data analysis Shanthalakshmi Revathy J., Uma Maheswari N., Sasikala S. Principles and Applications of Socio Cognitive and Affective Computing, 2022 Emotion recognition based on biological signals from the brain necessitates sophisticated signal processing and feature extraction techniques. The major purpose of this research is to use the enhanced BiLSTM (E-BiLSTM) approach to improve the effectiveness of emotion identification utilizing brain signals. The approach detects brain activity that has distinct characteristics that vary from person to person. This experiment uses an emotional EEG dataset that is publicly available on Kaggle. The data was collected using an EEG headband with four sensors (AF7, AF8, TP9, TP10), and three possible states were identified, including neutral, positive, and negative, based on cognitive behavioral studies. A big dataset is generated using statistical brainwave extraction of alpha, beta, theta, delta, and gamma, which is then scaled down to smaller datasets using the PCA feature selection technique. Overall accuracy was around 98.12%, which is higher than the present state of the art.
Deep learning based fusion model for COVID-19 diagnosis and classification using computed tomography images RT Subhalakshmi, S Appavu alias Balamurugan, S Sasikala Concurrent Engineering Research and Applications, 2022 Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis. This paper aims to develop a Deep Learning Based MultiModal Fusion technique called DLMMF for COVID-19 diagnosis and classification from Computed Tomography (CT) images. The proposed DLMMF model operates on three main processes namely Weiner Filtering (WF) based pre-processing, feature extraction and classification. The proposed model incorporates the fusion of deep features using VGG16 and Inception v4 models. Finally, Gaussian Naïve Bayes (GNB) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the DLMMF model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity of 96.53%, specificity of 95.81%, accuracy of 96.81% and F-score of 96.73%.
A Novel Software Package Selection Method Using Teaching-Learning Based Optimization and Multiple Criteria Decision Making A. S. Karthik Kannan, S. Appavu alias Balamurugan, S. Sasikala IEEE Transactions on Engineering Management, 2021 Software packages that meets the requirements of an organization should be appropriately investigated and evaluated. Picking up a wrong software package may adversely influence the business process and working function of an organization. Inappropriate software selection can turn out to be costly and it is a time-consuming decision-making process. This paper aims to provide a base for selecting the open source software packages based on analytic hierarchy process and technique for order preference similarity to ideal solution methodologies. In addition, the priority weights are generated and optimized by using teaching–learning based optimization approach. A well-organized algorithmic procedure is given in detail and a numerical example is examined to illustrate the validity and practicability of our proposed methodologies.
A secure cloud-based heterogeneous network using a novel routing protocol for smart military applications International Journal of Innovative Technology and Exploring Engineering, 2019
Ample feature selection algorithm for efficient prediction of main causes of aviation accident using tree based classifiers International Journal of Innovative Technology and Exploring Engineering, 2019
