This study proposes a deep learning-based framework for the automated detection of asthma and chronic obstructive pulmonary disease (COPD) using respiratory sound analysis. Breath sound recordings are preprocessed through resampling, silence trimming, and Wiener filtering to enhance signal quality. Short-Time Fourier Transform (STFT) is employed to convert audio signals into spectrogram representations, which are further augmented using time masking, frequency masking, and time warping to improve model generalization. The proposed model utilizes EfficientNet with multi-scale feature fusion to capture both local and global patterns in respiratory sounds. Experimental results demonstrate that the proposed approach achieves superior performance, with an accuracy of 99.21%, sensitivity of 99.56%, and specificity of 100%, outperforming existing CNN, ResNet, and SVM-based methods. The findings indicate that the proposed method is a reliable and efficient tool for non-invasive respiratory disease diagnosis.

The search for the periodicity in time-series database has a number of application, is an interesting data mining problem. In real world dataset are mostly noisy and rarely a perfect periodicity, this problem is not trivial. Periodicity is very common practice in time series mining algorithms, since it is more likely trying to discover periodicity signal with no time limit. We propose an algorithm uses FP-tree for finding symbol, partial and full periodicity in time series. We designed the algorithm complexity as O (kN), where N is the length of input sequence and k is length of periodic pattern. We have shown our algorithm is fixed parameter tractable with respect to fixed symbol set size and fixed length of input sequences. Experiment results on both synthetic and real data from different domains have shown our algorithms' time efficient and noise-resilient feature. A comparison with some current algorithms demonstrates the applicability and effectiveness of the proposed algorithm.