Near-infrared (NIR) spectroscopy has emerged as a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on histamine content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes, Safe and Unsafe, based on their histamine content. A comprehensive NIR dataset comprising 11,360 spectra collected at eight distinct positions within the fish body was obtained from 284 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their histamine content exceeded the permissible limit of 100 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques as well as ML algorithms were explored. The results demonstrated that utilizing NIR data specifically obtained from the tail’s flesh, a specific location within the fish, yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their second derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers with an accuracy of approximately 93%.

This paper describes the first attempt in developing a Vietnamese HMM-based Text-to-Speech system using the speaker-adaptive approach. Although speaker-dependent systems have been built widely, no speaker-adaptive system has been developed for Vietnamese so far. We collected speech data from several Vietnamese native speakers and employed state-of-the-art speech analysis, model training and speaker adaptation techniques to develop the system. Besides, we performed perceptual experiments to compare the quality of speaker-adapted (SA) voices built on the average voice model and speaker-dependent (SD) voices built on SD models, and to confirm the effects of contextual features including word boundary (WB) and part-of-speech (POS) on the quality of synthetic speech. Evaluation results show that SA voices have significantly higher naturalness than SD voices when the same limited contextual feature set excluding WB and POS was used. In addition, SA voices trained with limited contextual features excluding WB and POS still have better quality than SD voices trained with full contextual features including WB and POS. These results show the robustness of the speaker-adaptive over the speaker-dependent approach for Vietnamese statistical parametric speech synthesis.

Near-infrared (NIR) spectroscopy has become a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on urea content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes—Safe and Unsafe—based on their urea content. A comprehensive NIR dataset comprising 11,960 spectra collected from eight distinct positions within the fish body was obtained from 299 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their urea content exceeded the permissible limit of 1000 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques, as well as ML algorithms, were explored. The results demonstrated that utilizing NIR data specifically obtained from the outer skin of the stomach yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their first derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers, achieving an accuracy of up to 83.9%.

Human activity recognition (HAR) and fall detection play crucial roles in healthcare for the elderly and remote observation. This research presents three innovative deep learning models - MSRLSTM, MSRLSTM - Refined, and MSR - MultiHeadAttention-designed for HAR and fall detection by utilizing Inertial Measurement Unit data from the UP-Fall Detection Dataset. By employing convolutional neural networks, residual learning, and multi-head attention, these models effectively capture complex temporal and spatial patterns present in multimodal sensor data. When evaluated on the UP-Fall Detection Dataset, MSRLSTM-Refined and MSR - MultiHeadAttention achieved accuracies of 93.91% and 95.49%, respectively, outpacing the baseline MSRLSTM (92.10%). The MSR-MultiHeadAttention model stands out due to its precision and temporal modeling capabilities, while MSRLSTM-Refined delivers computational efficiency suitable for wearable devices. Although there are challenges in differentiating similar motion patterns, these models demonstrate significant potential for real-time fall detection, contributing to remote healthcare monitoring solutions and related fields.