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SSRN 2023年

作者： Xie, Tong Wan, Yuwei Zhou, Yufei Huang, Wei Liu, Yixuan Linghu, Qingyuan Wang, Shaozhou Kit, Chunyu Grazian, Clara Zhang, Wenjie Hoex, Bram School of Photovoltaic and Renewable Energy Engineering University of New South Wales KensingtonNSW Australia GreenDynamics Pty. Ltd KensingtonNSW Australia Department of Linguistics and Translation City University of Hong Kong Hong Kong School of Mathematics and Statistics University of Sydney CamperdownNSW Australia DARE ARC Training Centre in Data Analytics for Resources and Environments Australia School of Computer Science and Engineering University of New South Wales KensingtonNSW Australia

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather than their properties. This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in material science. We accomplished this task by fine-tuning LLaMA on an existing perovskite solar cell dataset with an 87.14% F1- score. The produced data extends the existing dataset with papers published since its release, enabling its direct utilization as input in subsequent data analysis. This feature empowers material scientists to develop models by selecting high-quality review articles within their domain. Additionally, we designed regression and classification tasks to predict the electrical performance of solar cells and design materials or devices with targeted parameters. Our results demonstrate comparable performance to traditional machine learning methods without feature selection, highlighting the potential of large language models to acquire scientific knowledge and design new materials akin to materials scientists. © 2023, The Authors. All rights reserved.

关键词： Perovskite solar cells

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Fair and explainable Myocardial Infarction (MI) prediction: Novel strategies for feature selection and class imbalance correction

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computers in Biology and Medicine 2025年 184卷 109413-109413页

作者： Akter, Simon Bin Akter, Sumya Tuli, Moon Das Eisenberg, David Lotvola, Aaron Islam, Humayera Fernandez, Jorge Fresneda Hüttemann, Maik Pias, Tanmoy Sarkar Martin Tuchman School of Management New Jersey Institute of Technology Newark NJ07102 United States Greenlife Medical College & Hospital Dhaka Bangladesh Department of Computer Science Virginia Tech College of Engineering BlacksburgVA United States Institute for Data Science and Informatics University of Missouri Columbia United States Department of Biochemistry Microbiology and Immunology Wayne State University School of Medicine DetroitMI United States Department of Information Management and Business Analytics Montclair State University Feliciano School of Business NJ United States Department of Oncology Wayne State University School of Medicine DetroitMI United States Center for Molecular Medicine and Genetics Wayne State University School of Medicine DetroitMI United States

The rising incidences of myocardial infarction (MI), often affecting individuals without traditional risk factors, highlight the urgent need for improved early detection using personal health data. However, health surveys and electronic health records (EHRs) frequently suffer from class imbalances, leading to prediction biases and differences between specificity and sensitivity, which hinder reliable model development despite the valuable insights contained in these datasets. To address this, we have introduced a novel approach to enhance MI risk prediction using self-reported attributes from the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) dataset. Our approach incorporates three innovative techniques: the Dual-Path Artificial Neural Network (DP-ANN) to mitigate biased decision making across imbalanced datasets, the Triple Criteria Selection (TCS) for unbiased feature selection, and Minority Weighted Sampling (MWS) to tackle challenges of uncontrolled minority class sampling. These methods collectively enhance MI prediction and feature relevance. The DP-ANN model has achieved balanced performance, with an average specificity of 80%, sensitivity of 82%, and AUC–ROC of 89.5%, improving imbalance variance by approximately 14.96% compared to prior studies. By outperforming other models across four heavily imbalanced datasets, our approach demonstrates robustness and generalizability. Additionally, SHapley Additive exPlanations (SHAP) analysis has revealed key predictors and risk factors for MI, such as coronary heart disease and bronchitis in females, and stroke among individuals aged 35–54. In conclusion, our study provides a robust model for healthcare professionals to assess MI risk through targeted factors, promoting early detection and potentially improving patient outcomes. © 2024

关键词： Electronic health record

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Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images 23rd

Renal Cell Carcinoma Detection and Subtyping with Minimal Po...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Gao, Zeyu Puttapirat, Pargorn Shi, Jiangbo Li, Chen School of Computer Science and Technology Xian Jiaotong University XianShaanxi710049 China National Engineering Lab for Big Data Analytics Xian Jiaotong University XianShaanxi710049 China

ISBN: (纸本)9783030597214

Cancerous region detection and subtyping in whole-slide images (WSIs) are fundamental for renal cell carcinoma (RCC) diagnosis. The main challenge in the development of automated RCC diagnostic systems is the lack of large-scale datasets with precise annotations. In this paper, we propose a framework that employs a semi-supervised learning (SSL) method to accurately detect cancerous regions with a novel annotation method called Minimal Point-Based (Min-Point) annotation. The predicted results are efficiently utilized by a hybrid loss training strategy in a classification model for subtyping. The annotator only needs to mark a few cancerous and non-cancerous points in each WSI. The experiments on three significant subtypes of RCC proved that the performance of the cancerous region detector trained with the Min-Point annotated dataset is comparable to the classifiers trained on the dataset with full cancerous region delineation. In subtyping, the proposed model outperforms the model trained with only whole-slide diagnostic labels by 12% in terms of the testing f1-score. We believe that our "detect then classify" schema combined with the Min-Point annotation would set a standard for developing intelligent systems with similar challenges. © 2020, Springer Nature Switzerland AG.

关键词： Intelligent systems

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Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of data: Results from Clinical Neuroscience

arXiv

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arXiv 2023年

作者： Wang, Rongguang Erus, Guray Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting

关键词： Neuroimaging

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An Industrial IoT Driven Approach for Sewage Gas Identification and Reporting System using Intelligent Sensors with Logical Controller

An Industrial IoT Driven Approach for Sewage Gas Identificat...

引用

Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： J. Rajasubha R Ramya A. Hency Juliet Janakiraman V S. Rukmani Devi Karuppaiah S Department of Computer Science and Engineering Dhanalakshmi College of Engineering and Technology Chennai Tamil Nadu India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Based on a real-time sewage gas level detection and monitoring system, this system seeks to offer intelligent solutions for the monitoring of toxic sewage gases. An alarm is issued to the observer who is looking at the circumstances from a distance whenever a specific threshold is crossed. Following this, the data is transmitted together with different gas ppm levels, indicating if the worker can clean or operate securely in that region. The suggested solution incorporates both the IoT platform and the remotely located IoT monitoring devices. This necessitates finding the appropriate threshold values for septic plants and facilities, as well as calibrating gas sensors for use in industry. The gear is meant to provide the sewage worker advance notification whenever the concentration of toxic gaseous substances grows over time, ensuring their safety. The presence of gas, temperature, and other factors in sewage may be detected using a variety of sensors. By notifying sewage workers as well as cleaners via SMS whenever the threshold value drops below the observed levels, this technology helps save people from harmful infections, which is a social good. A number of harmful gas concentrations are graphed out for real-time monitoring, and this is how it accomplishes its job. In order to assess the efficacy of the suggested model, this paper cross-validates it with the Conventional Sewage Gas Detection (CSGD) based on GPRS functioning. The goal of the cross-validation is to create an IoT based Sewage Gas Detection (IoTSGD) system that can monitor and analyze toxic gas levels in real-time and notify workers. Firebase receives the measured gas levels. If the levels rise over a certain point, it will notify the authorized individuals working remotely through their linked mobile devices. An SMS message can alert the authorities if the sewage system is likely to overflow. For the purpose of monitoring, an android app was created and connected with the planned system.

关键词： Temperature sensors Temperature measurement Gases Temperature Sewage treatment Message services Real-time systems

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Leveraging Probe data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Leveraging Probe Data and Machine Learning to Derive and Int...

引用

2023 IEEE International Conference on Big data, Bigdata 2023

作者： Jin, Ling Xu, Xiaodan Wang, Yuhan Sadabadi, Kaveh Farokhi Lazar, Alina Don, Duleep Rathgamage Needell, Zachary Spurlock, C Anna Amirgholy, Mahyar Asudegi, Mona Energy Technology Area Berkeley National Laboratory BerkeleyCA94720 United States University of Maryland Center for Advanced Transportation Technology College ParkMD20742 United States Youngstown State University Department of Computer Science and Information YoungstownOH44555 United States Kennesaw State University School of Data Science and Analytics MariettaGA30060 United States Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kennesaw State University Civil and Environmental Engineering Department MariettaGA30060 United States Office of Transportation Policy Studies Federal Highway Administration DC WashingtonDC20590 United States

ISBN: (纸本)9798350324457

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level. © 2023 IEEE.

关键词： Interaction Shapley values machine learning models macroscopic fundamental diagram TreeExplainer United States vehicle probe data

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Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance

arXiv

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arXiv 2025年

作者： Chen, Ziqi Peng, Bo Zhai, Tianhua Adu-Ampratwum, Daniel Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Medicinal Chemistry and Pharmacognosy The Ohio State University ColumbusOH43210 United States Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSMol to facilitate drug development. DiffSMol generates 3D binding molecules based on the shapes of known ligands. DiffSMol encapsulates geometric details of ligand shapes within pre-trained, expressive shape embeddings and then generates new binding molecules through a diffusion model. DiffSMol further modifies the generated 3D structures iteratively via shape guidance to better resemble the ligand shapes. It also tailors the generated molecules toward optimal binding affinities under the guidance of protein pockets. Here, we show that DiffSMol outperforms the state-of-the-art methods on benchmark datasets. When generating binding molecules resembling ligand shapes, DiffSMol with shape guidance achieves a success rate 61.4%, substantially outperforming the best baseline (11.2%), meanwhile producing molecules with novel molecular graph structures. DiffSMol with pocket guidance also outperforms the best baseline in binding affinities by 13.2%, and even by 17.7% when combined with shape guidance. Case studies for two critical drug targets demonstrate very favorable physicochemical and pharmacokinetic properties of the generated molecules, thus, the potential of DiffSMol in developing promising drug candidates. © 2025, CC BY-NC-SA.

关键词： Ligands

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W-net: A two-stage convolutional network for nucleus detection in Histopathology Image

arXiv

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arXiv 2021年

作者： Mao, Anyu Wu, Jialun Bao, Xinrui Gao, Zeyu Gong, Tieliang Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China

Pathological diagnosis is the gold standard for cancer diagnosis, but it is labor-intensive, in which tasks such as cell detection, classification, and counting are particularly prominent. A common solution for automating these tasks is using nucleus segmentation technology. However, it is hard to train a robust nucleus segmentation model, due to several challenging problems, i.e., the nucleus adhesion, stacking, and excessive fusion with the background. Recently, some researchers proposed a series of automatic nucleus segmentation methods based on point annotation, which can significant improve the model performance. Nevertheless, the point annotation needs to be marked by experienced pathologists. In order to take advantage of segmentation methods based on point annotation, further alleviate the manual workload, and make cancer diagnosis more efficient and accurate, it is necessary to develop an automatic nucleus detection algorithm, which can automatically and efficiently locate the position of the nucleus in the pathological image and extract valuable information for pathologists. In this paper, we propose a W-shaped network for automatic nucleus detection. Different from the traditional U-Net based method, mapping the original pathology image to the target mask directly, our proposed method split the detection task into two sub-tasks. The first sub-task maps the original pathology image to the binary mask, then the binary mask is mapped to the density mask in the second sub-task. After the task is split, the task’s difficulty is significantly reduced, and the network’s overall performance is improved. In order to evaluate the model efficiency in system application, we integrate the automatic nucleus detection model into the digital pathology image analysis system, Pathology Information Management & Integration Platform (PIMIP). Our proposed network can automatic identify the center of each nucleus. Combined with the NuClick, a semi-supervised recognition model

关键词： Information management

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Awareness requirement and performance management for adaptive systems: a survey

arXiv

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arXiv 2023年

作者： Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Qader, Shko Vimal, S. Chhabra, Amit Yaseen, Zaher Mundher Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientifc Research KRI Sulaymaniyah46001 Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Sydney Australia Sydney Australia Information Technology Department Kent Institute Australia Sydney Australia Department of Information Technology University College of Goizha Kurdistan Region Sulaimani Iraq Data Analytics Lab Department of Artifcial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran31621 Saudi Arabia Department of Computer Science College of Science Charmo University KRI Chamchamal46023 Iraq Department of Information Technology Computer Science Institute Sulaimani Polytechnic University KRI Sulaymaniyah46001 Iraq

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools). © 2023, CC BY-NC-ND.

关键词： Adaptive systems

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Interactive Remote Robot for Pediatric Patients

Interactive Remote Robot for Pediatric Patients

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： I’ zzul Labiqah Mohamad Azmi Murizah Kassim Norakmar Arbain Sulaiman Suhaili Beeran Kutty Marina Yusoff School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia Faculty of Computer and Mathematical Science Universiti Teknologi MARA Shah Alam Selangor Malaysia

Pediatric patients are defined based on the age of 21 years old or younger during the time of diagnosis or treatment. Patients that are traumatized by the unfortunate incident do not have a companion in the ward once admitted. Medical procedures may become invasive that could cause distress for the patients causing them to develop stress and anxiety before the upcoming procedure. The research has developed and tested an interactive robot to assist pediatric patients to undergo medical procedures. Speech recognition and body gestures was built in the robot to interact with the patients. The humanoid robot is programmed to maintain receiving responses from samples of pediatric patients with Artificial Intelligence (AI) which acted as social skills such as giving feedback and giving mutual gaze while communicating. An Arduino microcontroller was built into the robot to control the movements of the robot via a Bluetooth module. DC motors are used for robot movements that are connected to motor drivers due to the extra power supply required to operate. AI was created for communication skills with patients. The result presents an analysis of the communication performance of the robot and AI. The robot movement was presented, and AI answers communication was listed. Research has presented its significance in that the robot helps and assisted patients to entertain and interact with pediatric patients during hospital stay which motivates and cheer them.

关键词： Service robots Shape Friction Anxiety disorders Speech recognition DC motors Artificial intelligence

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