Respiratory-sound event classification is challenged by non-stationarity, class imbalance, heterogeneous acquisition, and participant-correlated recordings. This study evaluates whether direct fusion of short-time Fourier transform (STFT), mel-frequency cepstral coefficient (MFCC), and wavelet-packet features improves a temporal one-dimensional convolutional neural network (1D-CNN), and whether temporal convolution offers an advantage over conventional classifiers. A radial basis function support vector machine (RBF-SVM) and random forest were included deliberately to separate the value of the engineered representation from classifier complexity. Experiments used 920 recordings and 6898 annotated cycles from the International Conference on Biomedical and Health Informatics (ICBHI) 2017 Respiratory Sound Database. The predefined 60:40 recording-level benchmark partition was retained; model selection used five-fold participant-grouped cross-validation, and 95% confidence intervals were estimated from 1000 participant-level bootstrap resamples. The complete hybrid 1D-CNN achieved a macro-averaged F1-score of 0.313. MFCC alone yielded 0.320, but the paired difference was not statistically resolved. The RBF-SVM and random forest achieved 0.401 and 0.378, respectively. These findings apply to direct early concatenation with the shared 1D-CNN backbone and do not imply that feature fusion is generally ineffective. The study provides leakage-aware baselines, controlled ablations, clustered uncertainty estimates, and frozen artifacts for reproducible comparison.
In this study, the effect of data augmentation on machine learning (ML) models that predict the natural frequency of human long bones from the spectrogram of bone-conducted sound was investigated. Bone-conducted sound was recorded from the tibia of 45 healthy university students using a custom-developed hammering device. Short-Time Fourier Transform (STFT) was applied to the recorded sounds to generate spectrograms, from which six ML models were examined to predict the natural frequency. Ground-truth natural frequency was defined as peak values identified from Fast Fourier Transform (FFT) spectra obtained by recorded waveforms of sounds. Data augmentation was performed by shifting bone-conducted sound waveforms, expanded to 5 levels, i.e., 3-fold (3×) to 11-fold (11×). All models showed improved accuracy by data augmentations compared to the original dataset, with a particularly substantial improvement observed in many models when the dataset was expanded from the original to the 3× level. Among the ML models examined here, DenseNet-121 with 11× data augmentation achieved the lowest MAE and the highest R2 (10.00 ± 2.27 Hz; R2 = 0.927 ± 0.046), followed by PeleeNet, which is constructed on a lightweight DenseNet architecture, with 7× data augmentation (MAE = 10.37 ± 2.91 Hz, R2 = 0.920 ± 0.059).
Fault diagnosis in industrial equipment plays a crucial role in ensuring reliable system functionality and minimizing the costs associated with repair and maintenance. Traditionally, fault diagnosis has relied on human intelligence (HI), with skilled personnel applying knowledge and expertise based on reasoning and contextual understanding. Nevertheless, as modern industrial systems have grown more complex, operated at higher speeds, and generated massive amounts of data, relying solely on HI has become increasingly challenging and less scalable. Consequently, modern fault diagnosis has turned toward artificial intelligence (AI). This paper presents a comparative study of HI and AI in industrial fault diagnosis, based on a literature-driven analysis. The results confirm that while AI-based fault diagnosis systems perform well in processing large datasets and achieve improved diagnostic accuracy, these practices also face limitations related to data dependency, explainability, and deployment cost. By contrast, human intelligence remains indispensable in handling uncertain, rare, or new fault conditions that require contextual judgment and flexibility. The review further indicates that augmented intelligence (AuI) provides a collaborative framework that combines the complementary strengths of HI and AI for industrial fault diagnosis. Furthermore, emerging research directions, such as explainable and trustworthy AI, foundation models, large language models, physics-informed AI, digital twins, and human-centered AI, are identified as promising developments for next-generation intelligent diagnostic systems. The findings suggest that augmented intelligence is the most promising approach for advancing the performance and reliability of diagnostic systems in industrial machines.
The liver plays a crucial role in maintaining essential physiological functions; however, excessive alcohol consumption significantly increases the risk of liver disorders. Accurate and early prediction of such conditions is vital for timely intervention and effective clinical management. Nevertheless, liver disorder prediction is typically challenged by class imbalance, which makes the conventional fixed classification threshold (0.5) suboptimal for binary classification. To address these issues, this paper proposes an AI-driven two-stage feature fusion-based ensemble model for intelligent biomedical data processing and liver disorder prediction. In the first stage, multiple tree-based ensemble models are employed to evaluate feature importance, and a feature selection strategy is designed to select an optimal subset of discriminative features. In the second stage, prediction probabilities generated by these base learners are integrated with the selected feature subset to construct an enhanced feature space through feature-level information fusion. This probability-aware fusion strategy captures richer predictive information and alleviates the limitations of fixed-threshold binary classification. In addition, a meta-ensemble model is employed to aggregate heterogeneous predictive patterns from multiple learners for the final prediction based on an optimized threshold. Extensive experiments based on two benchmark liver disorder datasets demonstrate that the proposed model consistently outperforms the compared models in terms of predictive performance. Statistical analysis also confirms that the proposed model significantly outperforms the compared methods. These results indicate that the proposed model could serve as a useful AI-aided biomedical healthcare data processing tool for liver disorder prediction.
In the contemporary digital era, image watermarking is essential for protecting intellectual property due to the widespread unauthorized distribution of digital content. In this work, a robust and efficient image watermarking scheme for copyright protection is proposed. The method integrates wavelet packet decomposition (WPD) with an ensemble of bagged tree classifiers, forming the BT-WPD framework. In the proposed approach, wavelet packet coefficients extracted from each color channel are reorganized into structured batches that capture spatial frequency characteristics, enabling effective watermark embedding in the WPD domain guided by the bagged tree ensemble model. Experimental results demonstrate that the proposed method achieves high imperceptibility, with a peak signal-to-noise ratio (PSNR) exceeding 60 dB, while maintaining strong robustness against various image processing attacks. The method also exhibits low computational complexity during watermark extraction, making it suitable for practical applications. Furthermore, the framework is extended to support Quick Response (QR) code watermark embedding, demonstrating enhanced robustness and versatility for copyright protection in digital media systems.
