Customers demand typical type of products with multiple features. We want to develop a business intelligence system which helps the company to set the blue ocean strategy by discoveringk-most promising features (k-MPF) from the customers’ query and a set of existing products of the similar type. In this paper, we have formulatedk-MPF to set the blue ocean strategy with compatible features. We have experimented with our proposed algorithms using different synthetic and real datasets, and the results showed the effectiveness of our proposed algorithms.

Physicians could significantly augment their understanding of the stress trends exhibited by their patients through the in-depth analysis of continuously monito Physicians could significantly augment their understanding of the stress trends exhibited by their patients through the in-depth analysis of continuously monito

The rapid integration of artificial intelligence (AI) into medical informatics, particularly in the context of mental health data, can bring about significant transformations in healthcare decision-support systems. However, ensuring that AI gains widespread acceptance and is regarded as reliable in healthcare requires addressing critical issues concerning its robustness, fairness, and privacy. This chapter presents a comprehensive study that delves into the urgent need for dependable AI in medical informatics, explicitly focusing on collecting mental health data using sensors. The authors put forth a methodological framework combining cutting-edge AI techniques, leveraging deep learning models such as recurrent neural networks (RNN), including variants like LSTM and GRU, and ensemble techniques like random forest, AdaBoost, and XGBoost. Through a series of experiments involving healthcare decision support systems, the authors underscore the pivotal role of model overfitting in establishing trustworthy AI systems. Request access from your librarian to read this chapter's full text.

This study presents a temperature-conditioned deep generative approach for efficiently generating spin configurations of the two-dimensional (2D) Ising model. The method employs a convolutional generator network, conditioned on spin states, temperature embeddings, and random noise, to generate thermodynamically accurate spin configurations on a 2D square lattice. Physics-based constraints, derived from magnetization, energy, and nearest-neighbor correlations obtained via Monte Carlo simulations, are integrated as auxiliary loss terms during training to ensure physical consistency. The model accurately captures phase transition behaviors near the critical temperature and supports the scalable generation of synthetic configurations for further analysis. Investigations across lattice sizes L=16, 32, and 64 demonstrate close alignment with Monte Carlo baselines, with generated configurations achieving mean absolute errors as low as 0.0016 for magnetization, 0.0063 for energy, and 0.0016 for correlation in the L=64 case. Phase classification using standard machine learning models further validates the quality of the generated data, with ensemble classifiers yielding F1 scores above 0.99. These findings highlight the proposed framework as a scalable, computationally efficient solution for generating spin configurations in an Ising model.