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1. AI-driven value assessment for intelligent remanufacturing; Advances in remanufacturing 2024: Proceedings of VIII International Workshop on Autonomous Remanufacturing (Lecture Notes in Mechanical Engineering)

   Esmaeilian, Behzad; Behdad, Sara (September 2025, Springer)

   Rickli, Jeremy (Ed.)

   This paper aims to introduce an Artificial Intelligence (AI) guided computational framework for the automatic identification, inspection, assessment, and remanufacturing of end-of-use products. The proposed framework consists of three main steps: (1) developing computer vision and image processing algorithms for analyzing product teardown images, (2) quantifying the economic and environmental value of remanufacturing from product images, and (3) developing recommender algorithms to identify the best recovery decision for each device. The paper discusses the importance of advancing object detection, image segmentation, and machine learning algorithms to automatically compute the value embedded in discarded items and developing recommendation systems to determine remanufacturing operations from product configurations. The main focus of the paper is on the value assessment and remanufacturing of electronic waste (e-waste). The paper emphasizes the need for developing object detection for identifying small objects (e.g., screws, bolts, snaps) and overlapped components (e.g., cables, printed circuit boards) standard in the design of consumer electronics by incorporating product shapes and features. The proposed value assessment framework has applications beyond remanufacturing and can be used in take-back programs and other business models that benefit from product serialization and assessment of individual devices. 

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2. Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data

   https://doi.org/10.1093/molbev/msad216  

   Amin, Md Ruhul; Hasan, Mahmudul; Arnab, Sandipan Paul; DeGiorgio, Michael (October 2023, Molecular Biology and Evolution)

   Abstract Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data. 

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3. MKG-FENN: A Multimodal Knowledge Graph Fused End-to-End Neural Network for Accurate Drug–Drug Interaction Prediction

   https://doi.org/10.1609/aaai.v38i9.28887  

   Wu, Di; Sun, Wu; He, Yi; Chen, Zhong; Luo, Xin (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Taking incompatible multiple drugs together may cause adverse interactions and side effects on the body. Accurate prediction of drug-drug interaction (DDI) events is essential for avoiding this issue. Recently, various artificial intelligence-based approaches have been proposed for predicting DDI events. However, DDI events are associated with complex relationships and mechanisms among drugs, targets, enzymes, transporters, molecular structures, etc. Existing approaches either partially or loosely consider these relationships and mechanisms by a non-end-to-end learning framework, resulting in sub-optimal feature extractions and fusions for prediction. Different from them, this paper proposes a Multimodal Knowledge Graph Fused End-to-end Neural Network (MKGFENN) that consists of two main parts: multimodal knowledge graph (MKG) and fused end-to-end neural network (FENN). First, MKG is constructed by comprehensively exploiting DDI events-associated relationships and mechanisms from four knowledge graphs of drugs-chemical entities, drug-substructures, drugs-drugs, and molecular structures. Correspondingly, a four channels graph neural network is designed to extract high-order and semantic features from MKG. Second, FENN designs a multi-layer perceptron to fuse the extracted features by end-to-end learning. With such designs, the feature extractions and fusions of DDI events are guaranteed to be comprehensive and optimal for prediction. Through extensive experiments on real drug datasets, we demonstrate that MKG-FENN exhibits high accuracy and significantly outperforms state-of-the-art models in predicting DDI events. The source code and supplementary file of this article are available on: https://github.com/wudi1989/MKG-FENN. 

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4. An Explainable Artificial Intelligence Approach Using Graph Learning to Predict Intensive Care Unit Length of Stay

   https://doi.org/10.1287/isre.2023.0029  

   Guo, Tianjian; Bardhan, Indranil R; Ding, Ying; Zhang, Shichang (December 2024, Information Systems Research)

   We propose and test a novel graph learning-based explainable artificial intelligence (XAI) approach to address the challenge of developing explainable predictions of patient length of stay (LoS) in intensive care units (ICUs). Specifically, we address a notable gap in the literature on XAI methods that identify interactions between model input features to predict patient health outcomes. Our model intrinsically constructs a patient-level graph, which identifies the importance of feature interactions for prediction of health outcomes. It demonstrates state-of-the-art explanation capabilities based on identification of salient feature interactions compared with traditional XAI methods for prediction of LoS. We supplement our XAI approach with a small-scale user study, which demonstrates that our model can lead to greater user acceptance of artificial intelligence (AI) model-based decisions by contributing to greater interpretability of model predictions. Our model lays the foundation to develop interpretable, predictive tools that healthcare professionals can utilize to improve ICU resource allocation decisions and enhance the clinical relevance of AI systems in providing effective patient care. Although our primary research setting is the ICU, our graph learning model can be generalized to other healthcare contexts to accurately identify key feature interactions for prediction of other health outcomes, such as mortality, readmission risk, and hospitalizations. 

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5. Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models

   https://doi.org/10.1609/aaai.v38i21.30396  

   Islam, Khairul (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   The widespread use of Artificial Intelligence (AI) has highlighted the importance of understanding AI model behavior. This understanding is crucial for practical decision-making, assessing model reliability, and ensuring trustworthiness. Interpreting time series forecasting models faces unique challenges compared to image and text data. These challenges arise from the temporal dependencies between time steps and the evolving importance of input features over time. My thesis focuses on addressing these challenges by aiming for more precise explanations of feature interactions, uncovering spatiotemporal patterns, and demonstrating the practical applicability of these interpretability techniques using real-world datasets and state-of-the-art deep learning models. 

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