Intelligent Image and Signal Processing Using Integrated Deep Learning and Machine Learning Models

Deep learning (DL) and machine learning (ML) integration have demonstrated a real potential in intelligent image and signal processing tasks and have brought great improvements to the areas of computer vision and human activity recognition (HAR). This paper explores the application of hybrid models, which involve the use of deep learning to extract features and use them with conventional machine learning classifiers to improve the accuracy and performance of classification. In particular, the CIFAR-10 image classification data set and UCI HAR time-series data set were utilized to assess the opportunities of these hybrid models. The findings indicate that the hybrid method that relies on Convolutional Neural Networks (CNNs) to extract features and machine learning classifiers like logistic regression to make a classification is better than deep learning-only models in image classification and activity recognition. The CNN-LSTM hybrid model was also more accurate in human activity recognition because it could use both spatial information of sensor data and temporal relationships. Also, the paper emphasizes the applicability of hybrid models to a wide range of areas, such as medical diagnostics and classification of construction debris, and recognizes the problem of computational efficiency and overfitting. The results indicate that hybrid models can provide a good way out of enhancing the accuracy of classification and generalization in real-world applications, and future studies will focus on refining the models to be used in real-time and multi-modal data integration.