Mold on bread in the early stages of growth is difficult to discern with the naked eye. Visual inspection and expiration dates are imprecise approaches that consumers rely on to detect bread spoilage. Existing methods for detecting microbial contamination, such as inspection through a microscope and hyperspectral imaging, are unsuitable for consumer use. This paper proposes a novel early bread mold detection method through microscopic images taken using clip-on lenses. These low-cost lenses are used together with a smartphone to capture images of bread at 50× magnification. The microscopic images are automatically classified using state-of-the-art convolutional neural networks (CNNs) with transfer learning. We extensively compared image preprocessing methods, CNN models, and data augmentation methods to determine the best configuration in terms of classification accuracy. The top models achieved near-perfect $F_1$ scores of 0.9948 for white sandwich bread and 0.9972 for whole wheat bread.

Background: The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model’s complexity. In our systemAikyatan, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications’ combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).Results: We develop a suite of ML models, under the bannerAikyatan, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-‘a-vis the competition. Specifically,Aikyatan-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.Conclusions: Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. OurAikyatansuite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at:https://bitbucket.org/cellsandmachines/aikyatan.

Automated food recognition is essential in order to streamline dietary monitoring. To build and evaluate complex food recognition models, large datasets of annotated food images are crucial. In this paper, we introduce a new dataset called THFOOD-100, which is specifically designed for this purpose. This dataset consists of 53,459 high-quality images of popular Thai dishes categorized into 100 classes. We conducted a comprehensive comparison of 23 deep convolutional neural network and vision transformer architectures to establish a strong baseline for classification performance on the THFOOD-100 dataset. Additionally, we proposed training the models using cyclical learning rates, which has been shown to improve model generalization and significantly reduce training time. We demonstrated the effectiveness of cyclical learning rates with three standard optimizers on THFOOD-100, ETHZ Food-101, and UEC-Food256. The top-performing model achieved a 96% classification accuracy on THFOOD-100, showing great promise for real-world applications. Our new dataset is specifically aimed at better representing Thai cuisine in food recognition research, and our analyses offer valuable insights into the shortcomings of current models.

Obesity and diabetes represent escalating health challenges, exacerbated by the widespread availability and convenience of processed foods. Automated dietary monitoring technologies offer valuable support to individuals managing these conditions by helping them track and regulate their consumption, particularly of high-calorie, sugar-rich foods. Toward this goal, this paper presents KediNet, a hybrid deep learning architecture that combines the strengths of convolutional neural networks (CNNs) and vision transformers (ViTs) for Thai dessert recognition. Our main contributions include: 1) the proposal of KediNet, which outperforms nine state-of-the-art deep learning models; 2) a comprehensive comparison of classification performance across different model architectures, image resolutions, and data augmentation techniques; and 3) the creation of THDESS-20, a new publicly available dataset containing 5,242 images across 20 classes of Thai desserts. Experimental results demonstrate that KediNet achieves 98.64% balanced accuracy at $224\times 224$ resolution, surpassing all other tested models even at higher resolutions, while maintaining inference times suitable for mobile applications. Our analysis shows that KediNet effectively extracts meaningful features from Thai dessert images despite their high intra-class variation and visual similarities between classes. This work advances the field of food recognition for health applications while addressing the specific challenges of ethnic cuisine classification.

Food ingredient prediction from images is a challenging multi-label classification task with significant applications in dietary assessment and automated recipe recommendation systems. This task is particularly difficult due to highly imbalanced classes in real-world datasets, where most ingredients appear infrequently while several common ingredients dominate. In such imbalanced scenarios, the F-score metric is often used to provide a balanced evaluation measure. However, existing methods for training artificial neural networks to directly optimize for the F-score typically rely on computationally expensive hyperparameter optimization. This paper presents a novel approach for direct F-score optimization by reformulating the problem as cost-sensitive classifier optimization. We propose a computationally efficient algorithm for estimating the optimal relative cost parameters. When evaluated on the Recipe1M dataset, our approach achieved a micro F1 score of 0.5616. This represents a substantial improvement from the state-of-the-art method’s score of 0.4927. Our F-score optimization framework offers a principled and generalizable solution to class imbalance problems. It can be extended to other imbalanced binary and multi-label classification tasks beyond food analysis.