Informatics Publishing Limited

Akabarsaheb Babulal Nadaf ¹, Pravin Prakash Adivarekar ², Vijay Kumar Dwivedi ³, Dharmendra Kumar Yadav ⁴

Affiliations
1 Department of Computer Applications, Abhijit Kadam Institute of Management and Social Sciences, IN
2 Department of Computer Engineering, A.P. Shah Institute of Technology, IN
3 Department of Mathematics, Vishwavidyalaya Engineering College, IN
4 Department of Mathematics, Lalit Narayan Mithila University, IN

Abstract

Multimedia data, encompassing images, videos, and audio, has become a cornerstone in various AI-driven applications, particularly in pattern recognition tasks. The increasing complexity and volume of multimedia data necessitate robust and scalable analytical approaches. Traditional pattern recognition techniques often struggle to effectively manage the high-dimensional and multimodal nature of multimedia data. This study addresses the challenge by leveraging linear regression for analyzing multimedia data to enhance AI-driven pattern recognition. The proposed method integrates linear regression models with feature extraction techniques to identify and map underlying patterns within the multimedia data. The process begins with preprocessing steps, including normalization and dimensionality reduction, to ensure data consistency and manage computational complexity. Subsequently, linear regression models are applied to establish relationships between the extracted features and predefined classes or labels. The model’s performance is evaluated using precision, recall, and F1-score metrics. Experimental results on a benchmark multimedia dataset reveal that the proposed approach achieves an average accuracy of 92.4%, with a precision of 91.8% and a recall of 93.1%. These results outperform several state-of-the-art methods, demonstrating the model’s efficacy in accurately recognizing patterns within diverse multimedia data. Furthermore, the model exhibits scalability, maintaining high performance even when applied to large-scale datasets, thus validating its potential for real-world AI applications. The study concludes that linear regression, when integrated with appropriate feature extraction and preprocessing techniques, offers a viable solution for enhancing AI-driven pattern recognition in multimedia data.

Keywords

Multimedia Data, Pattern Recognition, Linear Regression, Feature Extraction, AI-Driven Applications

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Sachin Vasant Chaudhari ¹, T. S. Sasikala ², R. K. Gnanamurthy ³, Vijay Kumar Dwivedi ⁴, Davinder Kumar ⁵

Affiliations
1 Department of Electronics and Computer Engineering, Sanjivani College of Engineering, IN
2 Department of Computer Science and Engineering, Amrita College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
4 Department of Mathematics, Vishwavidyalaya Engineering College, IN
5 Micron Technology, US

Abstract

In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time

Keywords

Plant Disease Prediction, Neuro-Fuzzy-Genetic Algorithm, Optimization, Machine Learning, Classification, Feature Extraction.

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