International Journal of
Cyber-Quantum Systems
& Intelligent Machines

personSandhya G

This paper proposes an advanced handwritten document processing system, which is designed to enhance the handwritten text recognition by jointly using a Convolutional Neural Networks and Auto-Encoding Transformer models with BERT-based spelling correction and GECToR-based grammatical correction, and is further compared to an existing OCR-based approach for handwritten recognition. Materials and Methods: The results of this study consist of two groups: Group 1 consists of an experimental group that used the already- existing OCR system, which was tested against ten test samples, averaging about 78% accuracy in recognition. Group 2 presents the proposed CNN + Auto-Encoding Transformer model integrated with BERT and GECToR. Testing criteria included accuracy, error rate, and processing time. Sample size in both groups was determined through prior studies. It was expected to have a test power of 80%, with a significance level of 0.05 and a 95% confidence interval. Statistical analysis was done using SPSS 26.0 software; for comparing two independent samples, the appropriate statistical tool is the independent samples t-test. Result: The results show clearly that the proposed approach of utilizing the CNN Auto Encoding Transformer performs much better than a traditional approach of utilizing an Optical Character Recognition (OCR) system. The proposed approach achieves high accuracy of 93.6%, reduces errors to 0.11, and also speeds up recognizing handwritten text in less than 0.58 seconds while maintaining statistically significant improvements to confidence level of (p < 0.001). Conclusion: Overall, the combined effect of the CNN + Auto-Encoding Transformer with the use of BERT and GECToR makes the recognition of the handwritten text much more precise and even easier to read. The system is faster compared to the traditional OCR methods.

personJeevanantham M

The objective of this study is to design a CNN-based multimodal emotion recognition system using speech and ECG signals to improve recognition accuracy. Group 1 represents the existing speech-based emotion recognition system using MFCC features with a CNN model. Group 2 represents the proposed multimodal system combining MFCC speech features and ECG signals using CNN-based fusion. The evaluation of the system was done using Accuracy, Precision, Recall, and F1-score measures. The multimodal system's accuracy was higher, i.e., 92.8%, than the existing system, which is based on speech alone, with an accuracy of 78.5%. The results proved that the system using speech and ECG is more accurate and reliable, making it appropriate for emotion recognition in real-time applications.

personKaranSingh D

In this study, an AI-driven investment recommendation system is designed using a Retrieval-Augmented Generation (RAG) approach for generating context aware, interpretable information. The personalized investment recommendations compared with a DRL-based approach designed in a previous study. The study proposes two models of the three. Group 1 comprises an existing DRL-based investment recommendation system that learns market decisions using reward-based market learning. Group 2 comprises the proposed RAG-based system which combines financial knowledge retrieval (market reports, news). And market history) with a transformer-based language model to generate personalized investment recommendations. The models are tested using Accuracy, Recall, Precision, F1-Score, Return Efficiency, Processing Time and Interpretability Score. The statistical tests are performed with SPSS 26. 0 and a one-sample t-test that considers the significance at 0. 05, with a 95% confidence interval. The obtained result reveals that the proposed RAG based investment recommendation system significantly outperforms the DRL-based system with attainment of 88. 63% accuracy, 87. 40% recall, 87. 40% return efficiency, processing time of 312. 50 ms and an interpretability score of 79. 85% statistically significant, p<0.01. The result suggests that the RAG-based system has higher accuracy, improved returns and improved interpretability than the DRL-based System and thus is a more effective and efficient solution for AI-based investment advisory systems.