The rapid advancement of Artificial Intelligence (AI) and Deep Learning (DL) technologies has transformed industrial automation by enabling intelligent, adaptive, and self-optimizing systems. This paper proposes a two-level hybrid architecture for smart industrial automation based on AI-driven deep learning models. The architecture integrates both machine-level and system-level intelligence to enhance operational efficiency, fault detection, predictive maintenance, and decision-making in real time. At the first level, deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are employed for data sensing, pattern recognition, and anomaly detection from industrial Internet of Things (IIoT) devices. The second level utilizes AI-based control mechanisms, including reinforcement learning and expert systems, to optimize process automation and resource management. The hybrid design ensures scalability, adaptability, and interoperability across various industrial domains. Experimental and analytical evaluations demonstrate improved accuracy, reduced downtime, and energy-efficient performance. This approach signifies a step toward achieving fully autonomous, intelligent, and sustainable industrial ecosystems.

Keywords : Artificial Intelligence, Deep Learning, Industrial Automation, Hybrid Architecture, Smart Manufacturing

Authors : K. Sujatha

Title : Medical Assistance through Data Analysis using Big Data

Volume/Issue : 2020;2(6 (November - December))

Page No : 21 - 25

The rapid advancement of Artificial Intelligence (AI) and Deep Learning (DL) technologies has transformed industrial automation by enabling intelligent, adaptive, and self-optimizing systems. This paper proposes a two-level hybrid architecture for smart industrial automation based on AI-driven deep learning models. The architecture integrates both machine-level and system-level intelligence to enhance operational efficiency, fault detection, predictive maintenance, and decision-making in real time. At the first level, deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are employed for data sensing, pattern recognition, and anomaly detection from industrial Internet of Things (IIoT) devices. The second level utilizes AI-based control mechanisms, including reinforcement learning and expert systems, to optimize process automation and resource management. The hybrid design ensures scalability, adaptability, and interoperability across various industrial domains. Experimental and analytical evaluations demonstrate improved accuracy, reduced downtime, and energy-efficient performance. This approach signifies a step toward achieving fully autonomous, intelligent, and sustainable industrial ecosystems.

Keywords : Artificial Intelligence, Deep Learning, Industrial Automation, Hybrid Architecture, Smart Manufacturing

Authors : K. Sujatha

Title : Artificial Intelligence based Two Level Hybrid Architecture Smart Industrial Automation based on Smart Deep Learning

Volume/Issue : 2020;2(6 (November - December))

Page No : 14 - 20

Disaster management requires rapid, accurate decision-making based on diverse and often unstructured data sources. Machine learning (ML) algorithms, particularly Naïve Bayes and Decision Trees, offer effective computational frameworks for predicting, classifying, and managing disaster-related information. This paper presents an architectural approach for applying these algorithms in disaster management systems. The Naïve Bayes model provides a probabilistic classification mechanism suitable for early warning systems and risk assessment, utilizing historical and real-time data to predict disaster likelihood. In contrast, Decision Trees offer a hierarchical decision-making structure that aids in resource allocation, damage estimation, and response prioritization. The proposed architecture integrates data collection, preprocessing, model training, and decision-support modules to enhance situational awareness and response efficiency. Comparative analysis indicates that combining both algorithms can improve accuracy, interpretability, and computational speed. The study emphasizes the role of ML-driven architectures in enabling proactive disaster management, supporting both predictive analysis and adaptive response strategies.

Keywords : Disaster management, Machine learning, Naïve Bayes, Decision Trees, Predictive modeling

Authors : K. Sujatha

Title : Architecture approach of Machine Learning Algorithms NAVIE BAYES and Decision Trees for Disaster Management

Volume/Issue : 2020;2(6 (November - December))

Page No : 10 - 13

Stock price forecasting is a critical task in financial analytics, aiming to predict future price movements and assist investors in making informed decisions. Traditional statistical approaches often struggle to capture the complex, nonlinear, and dynamic nature of financial markets. Supervised learning methods, a subset of machine learning, have shown significant potential in modeling these intricate relationships by learning from historical data. This paper reviews various supervised learning techniques applied in stock price trend forecasting, including Linear Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and Artificial Neural Networks (ANN). Each method’s performance is analyzed in terms of prediction accuracy, robustness, and computational efficiency. The study also highlights the importance of feature selection, data preprocessing, and time-series modeling in enhancing prediction reliability. Furthermore, the integration of hybrid and ensemble models is discussed as a promising direction for improving trend forecasting. The review concludes that supervised learning offers a powerful framework for understanding stock market behavior and developing intelligent financial prediction systems.

Keywords : Stock forecasting, Supervised learning, Machine learning, Financial prediction, Time-series analysis

Authors : K. Sujatha

Title : Stock Price Trends Forecasting using Supervised Learning Methods

Volume/Issue : 2020;2(6 (November - December))

Page No : 4 - 9

Text mining, a crucial field within data mining, focuses on extracting meaningful patterns, trends, and knowledge from unstructured textual data. With the exponential growth of digital content, effective classification techniques have become essential for organizing and interpreting large text datasets. This paper reviews various data mining classification techniques applied in text mining, including Decision Trees, Naïve Bayes, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Neural Networks. Each method offers unique strengths in handling high-dimensional text data and improving the accuracy of document categorization, sentiment analysis, and topic detection. The study examines the comparative performance of these algorithms based on factors such as precision, recall, computational efficiency, and adaptability to large-scale datasets. Additionally, it discusses recent advancements in hybrid and ensemble models that combine multiple classifiers to enhance predictive performance. The review concludes that the selection of an appropriate classification technique largely depends on the nature of the text data, feature representation methods, and the specific application domain.

Keywords : Text mining, Classification, Data mining, Machine learning, Document analysis

Authors : K. Kantha Raju

Title : Data Mining Classification Techniques on the Analysis of Text Mining

Volume/Issue : 2020;2(6 (November - December))

Page No : 1 - 3