Introduction to Intelligent Computational Technologies C. Geetha, S. Sajithra, S. Srijayanthi, B. Reena, I. Subha, N. Sreelakshmi Predictive Methods in Next Generation Computing an Approach Toward Sustainability, 2026 Indeed, the convergence of smart technologies and sustainability imperatives is one of vital spectrum and focus in application development within the contemporary technological context. This chapter investigates the overlap in these domains by putting forward a framework for designing smart and sustainable applications via computational techniques. Building on cutting-edge Artificial Intelligence, Machine Learning, and Data Analytics technologies, it tackles challenging problems as efficiently as possible by maximizing resource utilization without compromising the environmental impact. The paper highlights some key concerns associated with the design, such as data acquisition, modeling, optimization, and deployment policy. Moreover, it discusses case studies and applications in different domains to clarify the effectiveness of intelligent computational techniques and their potential for enabling smart, sustainable development. By doing so, developers and stakeholders can work towards building novel pathways that lead to a more efficient, resilient, and environmentally conscious future. This chapter is all about gathering the right sets of elements/attributes or factors to utilize for different E-Governance services. As per this research, weak adoption factors of E-Governance have been identified and ranked using the fuzzy conjoint technique. These factors are ranked based on satisfaction levels, from highest to lowest: very satisfied, satisfied, neither/nor (ambiguous), dissatisfied, and very dissatisfied. The ranking of the above factors with satisfaction levels also defines whether the government needs to focus on or not to increase adoption.
Exploring the convergence of artificial intelligence and sustainable computing C. Geetha, K. Sujatha, A. Siva Kumar, T. Chandrasekar, S. D. Lalitha, S. Nithi Ravya Ethical Impacts of Using AI for Sustainable Development, 2025 Achieving sustainability and reducing inequities are at the heart of the 17 Sustainable Development Goals set out by the United Nations. When faced with complicated problems, a multidisciplinary team using data-driven methodologies, artificial intelligence, and technology may maximise efficiency, incorporate sustainability considerations, and back up well-informed decisions. This study classifies AI-driven solutions to sustainability problems by doing a literature review, scientometric analysis, and semantic analysis. It emphasises the interface of artificial intelligence and sustainability, as well as important research subjects and regional variations. Based on the results, it's clear that multi-dimensional decision-making requires hybrid methods that incorporate AI, data analytics, and human expertise. Also, the report stresses the significance of making sure AI and big data are used sustainably and the ethical considerations that come with it. To fill in knowledge gaps and propel successful sustainability solutions, collaboration and inclusive research are crucial.
Advanced Skin Lesion Classification Using Generative AI and Deep Learning Techniques Chenna Kesavan K, Balaji M, Harish S, Geetha C 2nd International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2025, 2025 Skin cancer, an abnormal lesion growth, is one of the deadliest types of cancer. The accurate diagnosis and classification of skin lesions while distinguishing the malignant tumors from dermoscopic images are among the challenging tasks set by the professional dermatologists. This research seeks to employ advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) in combination with Generative Adversarial Networks (GANs), toward efficacious improvement of skin lesion classification and cancer detection. The dataset was processed using the ISIC 2024 benchmark and includes steps such as resizing, noise reduction, and data augmentation. GANs afforded synthetically generated training image data, effectively increasing both the augmentation in size and diversity of the training set as well as addressing the common challenge of class imbalance. Pre-Trained models conceived such as Resnet50 and Inceptionv3 (GoogLeNet) use transfer learning to extract hierarchical features from images and categorize skin lesions into malignant or benign classes. The results from these models were merged together to attain higher accuracy in the classification. Using the GANs significantly boosted these CNN models’ performance by enriching training data with synthetic but realistic samples, achieving a higher accuracy. This approach would avail an accurate and automated system for skin lesion classification, which in turn may assist in their early diagnosis regarding skin cancer by classifying them as malignant or benign, besides enhancing clinical decision-making with improved patient outcomes. Such applications of GANs for data augmentation emphasize their ability to address data limitations in medical imaging.
A Design and Development of Enhanced MQTT Security Protocol and Blockchain Sharding in Edge Computing Networks K. Malathi, G P Susanna Wesley, Ala'a Al-Shaikh, B Suresh, Swathi Dendi, C Geetha Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025 To improve authentication, data integrity, and energy efficiency in Internet of Things (IoT) - Power Line Communication (PLC) systems, we designed to enhanced MQTT security & blockchain sharding for edge computing networks EMSPBE Model. The EMSPBE architecture introduces a multi-layered security model that consists of a data verifier biolog that also provides payload confidentiality through an AES encrypted payload. Communication takes place within a Brokerbased MQTT system and is governed by a User Managed Access (UMA) framework, which enables granular access control while minimizing latency. A Remote Hub System (RHS) serves as an edge node conducting local data analytics to minimise cloud dependency. A modified MQTT protocol, PrioMQTT, for timely data prioritisation and address scalability and transaction demand through Blockchain sharding for efficient consensus and enhanced data privacy. To optimise routing and lessen delays within the IoT-PLC network, a Node Shortest Path (NSP) Algorithm improves selection paths with recalibrated routes. In the performance analysis of QoS scenarios, for QoS-0, it recorded a connection setup duration of 0.41 ms, slightly higher than DMIEI (0.22 ms). In QoS-1, the latency was 0.77 ms compared to a maximum of 0.64 ms for the other methods. For QoS-2, the latency of 1.37 ms has high CPU efficiency at just 12 % usage compared to higher utilisation from RSMID (15 %), MCLIA (22 %), and DMIEI (32 %). EMSPBE utilised only 85 MB of memory. A message under 10,000 bytes, EMSPBE achieved a latency of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3-5 \text{ms}$</tex>, and it maintained competitive latencies (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$20-28 \text{ms}$</tex> for 50,000 bytes and 4549 ms for 80,000 bytes).
Machine Learning-Driven Predictive Diagnostics Framework for Fault Detection in Industrial IoT Systems K Arthishwari, Y Murali Krishna, CH Priyanka, Maram Y. Al-Safarini, Sravanthi, C Geetha Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025 The fast development of the Industrial Internet of Things (IIoT) systems made it possible to monitor and control the work of the industry in real-time; nevertheless, the traditional ways of fault detection, which rely on thresholding and rule-based approaches, do not usually allow working with the massive heterogeneous sensor data and changing operating conditions. Current models, like Support Vector Machines (SVM), Decision Trees, and simpler and simpler Neural Networks, do not have an adaptive learning quality and are not very competent in holding their own in non-stationary settings. In overcoming these shortcomings, the current study presents a Machine Learning-based predictive diagnostics framework (ML-PDF) to IIoT systems to detect faults early and predict them. The suggested architecture incorporates combined feature extraction with Principal Component Analysis (PCA) and Autoencoders based dimensionality reduction, and a stacked ensemble architecture comprising of the Random Forest (RF), Gradient Boosting (GBM), and Long Short-Term Memory (LSTM) networks to recognize temporal fault patterns. Moreover, an adaptive anomaly scoring system proposed on the principles of Bayesian inference reflects the fault probabilities dynamically so that the system could be reliable and resilient. The model is trained and tested on real-time IIoT data of industrial machines (e.g. rotating motors, pumps, compressors) and benchmark datasets of NASA turbofan engine degradation dataset. The experimental findings prove that the suggested MLPDF allows achieving a fault detection rate of 98.6 %, a 35 % decrease in the false alarm rate, and a 42 % increase in predictive maintenance scheduling efficiency in contrast to traditional ML methods. The importance of the work is that it allows conducting proactive diagnostics and practical decision-making, reducing downtime, minimizing maintenance expenses, and providing continuity of operations in smart industry settings. The proposed framework can also be used as a scalable and smart base of the next-generation self-healing IIoT infrastructures.
AI Lip Reader Detecting Speech Visual Data with Deep Learning Geetha C, Rohan Jai D, Sandheep Krishna A, Seelam Sai Vara Prasad Reddy 2024 4th International Conference on Intelligent Technologies Conit 2024, 2024 Introducing an AI-driven lipreading system adept at decoding speech across diverse languages, including English, Tamil, and Telugu. Leveraging a deep learning architecture comprising a 3D Convolutional Neural Network (3DCNN) with Bidirectional Long Short-Term Memory (BiLSTM) units, the model achieves remarkable accuracy in transcribing spoken words solely from visual cues provided by lip movements. Addressing critical accessibility needs, this system holds promise for applications in assistive technologies and human-computer interaction systems. Through rigorous experimentation and evaluation, the lipreading model demonstrates an impressive overall accuracy of 98.4%, underscoring its efficacy and robustness in recognizing spoken words across multiple languages. Advanced evaluation techniques, including ROC curves, confusion matrices, and classification reports, provide comprehensive insights into the model’s performance, enabling targeted refinements and optimizations. This work represents a significant advancement in the field of lipreading, offering a valuable contribution to multimodal communication and fostering inclusivity in diverse linguistic contexts.
Decoding AI: Experimental Analysis of Artificial Intelligence based Wine Quality Prediction Logic using Convoluted Deep Classification Strategy C Geetha, B. Arunsundar, G. Vasumathi, Thamizhazhakan K, C. Venkata Sudhakar Proceedings of 9th International Conference on Science Technology Engineering and Mathematics the Role of Emerging Technologies in Digital Transformation Iconstem 2024, 2024 Wine quality prediction is essential for optimizing production and ensuring consumer satisfaction. This study introduces a novel Convoluted Deep Classification (CDC) approach for accurate wine quality assessment. Leveraging convolutional neural networks (CNNs), our method effectively extracts hierarchical features from diverse wine data. The process begins with dataset preprocessing, encompassing chemical properties and sensory attributes. Subsequently, a hierarchical CNN architecture is tailored to capture intricate features such as acidity levels and volatile compounds. Techniques like data augmentation and transfer learning are employed to bolster model generalization. Evaluation on a benchmark dataset demonstrates outstanding performance, achieving a remarkable accuracy of 97%. This surpasses traditional methods and baseline CNN architectures, affirming the efficacy of our approach. Additionally, interpretability analyses unveil the key features influencing wine quality predictions, providing invaluable insights for producers and connoisseurs alike. Our CDC framework offers a robust and interpretable solution for wine quality prediction, empowering stakeholders with actionable insights for production optimization and quality assurance in the wine industry.
Automated and Decentralized Cloud based water level audit system with loT S. Selvakumar, C. Geetha, R. Vidhya Muthulakshmi, Sreevardhan Cheerla, Ashok Kumar, Sudhir Joshi Proceedings of 9th International Conference on Science Technology Engineering and Mathematics the Role of Emerging Technologies in Digital Transformation Iconstem 2024, 2024 Water bodies play a fundamental role in the development of society. The construction of dams for water storage has been carried out since ancient civilizations and, along with the development of modern society, new forms of construction have emerged. Over time, these constructions led to improvements for the population which, in turn, took the price of changing the environment and its effects. On this paper a water level audit system was developed with hardware for sensing and sending the data through the cellular network to a server for storage and later displaying it to the user so that he doesn't need to go back to the essay location, being useful for research on the local hydraulic system.
Control of Autonomous Underwater Vehicles M. P. Karthikeyan, S. Anitha Jebamani, P. Umaeswari, K. Chitti Babu, C. Geetha, S. Kirupavathi Artificial Intelligence for Autonomous Vehicles, 2024
Smart Tool for Earlier Prediction of Breast Cancer Using AI Geetha C, Anusha R, Prabhu V, Abinaya Kamatchi S, Gejashree T 2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023
An Improved Brain Tumor Detection Using Convolutional Neural Networks Geetha C, Prabhu V, Bandla Venkata Akash, Bhuvanendra Chowdary V, Billu Dilip, Ganta Avinash 2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023
FDR: An Automated System for Finding Missing People C. Geetha, Leelavathi. V, Meharunissa. R, Nivedita. V Proceedings of International Conference on Technological Advancements in Computational Sciences Ictacs 2022, 2022
Monitoringand detecting disease in human adults using fuzzy decision tree and random forest algorithm International Journal of Recent Technology and Engineering, 2019
