Abstract
The development of Artificial Intelligence (AI) models with an accurate prediction of human facial expression has become a significant challenge for the cases in which masks and sunglasses cover critical facial areas. Given that a substantial portion of human interactions involves non-verbal communication, accurately detecting human emotions such as anger, fear, disgust, happiness, sadness, and surprise would benefit a wide range of applications, from security assessments to psychological treatments. As a workaround, the current study explores the performance of a novel multi-channel arrangement comprised of a Haar-wavelet, Histogram of Oriented Gradients (HOG), and grayscale filters to improve the predictions of deep Convolutional Neural Network (CNN) on occluded results. This study uses the FER-2013 dataset and produces occluded samples by applying a virtual mask that covers almost 55% of facial areas comprising the mouth, lips, and jaw locations. Further investigations, including the impact of each filter, utilizing pre-trained models on occluded samples (transfer learning), and comparison to prior models are also carried out. The proposed approach yields an accuracy rate of 71% for non-occluded and 66% for the occluded samples, which are 6% to 11% higher than the base model. Further transfer learning technique increases the accuracy metrics by 18%, indicating that non-occluded pre-trained models can reveal a broader range of features and their relation, which to some extent compensates for the removed features due to the occlusion. These results suggest the potential capabilities of the proposed technique for similar imaging applications.

Abstract
This study presents an explainable content-based image retrieval system for predicting post-surgical aesthetic outcomes in breast cancer patients, comparing state-of-theart vision transformers, convolutional neural networks, and B-cos architectures. Results show that vision transformers, particularly GC ViT and DaViT, outperform convolutional neural networks and B-cos architectures, achieving an adjusted discounted cumulative gain of up to 80.18%. This superior performance is attributed to their ability to model long-range dependencies while effectively capturing local information. Bcos networks underperform (64.28-70.19% adjusted discounted cumulative gain), likely due to oversimplified feature alignment unsuitable for clinical tasks. Explainability analysis using Integrated Gradients reveals that models primarily focus on breast regions but occasionally attend to irrelevant features (e.g., arm positioning, leading to retrieval errors and highlighting a semantic gap between learned visual similarities and clinical relevance. Future work aims to integrate anatomical segmentation and ensemble learning methods to enhance clinical alignment and address attention inaccuracies. Clinical Relevance-The content-based image retrieval system developed in this study aids clinicians by supporting surgical outcome prediction in breast cancer patients and streamlining the traditionally time-intensive task of manually identifying similar reference images for patient consultation. © 2025 IEEE.

Abstract
This study employs Integer Linear Programming (ILP) to optimize gross profit for a local coffee shop, addressing challenges in inventory management and sale revenue optimization. A dataset comprising of 40 menu items and 34 ingredients was developed, incorporating constraints such as capital budget, ingredient availability, costs, and sales ratios to simulate monthly revenue. By applying the ILP methodology, the study achieved a gross profit margin of 42.28% of total sales revenue within a single month, underscoring its efficacy in improving profitability. The sensitivity analysis indicated that an increase in budget resulted in a proportional rise in sales revenue and gross profit, while inventory costs escalated at a comparatively slower pace. The research pinpointed high-performing items, including coffee, tea, and cold beverages, as significant contributors to profit, thereby highlighting the necessity for effective inventory management.

Abstract
Aesthetic outcomes in plastic and oncological surgery play a fundamental role in restoring patients' self-esteem, social engagement, and overall quality of life. Yet, managing pre-operative expectations and objectively assessing post-operative results remain as difficult challenges, compounded by the subjective nature of beauty and the scarcity of standardized evaluation tools. To address these challenges, we conduct a systematic review assessing computational methods for the prediction and evaluation of the aesthetic outcomes of plastic and oncological surgery, adhering to PRISMA guidelines. We propose a goal-oriented taxonomy that partitions computational approaches into two main categories: (1) prediction methods that pre-operatively predict the results of surgery through retrieval-based systems, generative artificial intelligence and advanced 3D modeling techniques, and (2) evaluation strategies that assess the post-operative outcomes through objective measurements, traditional machine learning, and deep learning models. Our synthesis indicates a potential paradigm shift from early work that relied on manual image annotation and manipulation to recent research that predominantly employs artificial intelligence. Nevertheless, over 90% of datasets remain private, and validation processes diverge among techniques with similar goals, limiting reproducibility and fair methodological comparisons. We conclude by advocating for the creation of larger publicly accessible datasets, integration of vision-language models to capture patient-reported outcomes, and rigorous clinical validation to ensure equitable, patient-centered care. By bridging computational innovation with clinical practice, this study contributes towards a more transparent, reliable, and personalized aesthetic outcome prediction and assessment.