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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Multiclass skin lesion classification using deep learning networks optimal information fusion

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Discover Applied sciences 2024年第6期6卷 1-13页

作者： Khan, Muhammad Attique Hamza, Ameer Shabaz, Mohammad Kadry, Seifeine Rubab, Saddaf Bilal, Muhammad Abdullah Akbar, Muhammad Naeem Kesavan, Suresh Manic Department of Computer Science HITEC University Taxila Pakistan Model Institute of Engineering and Technology Jammu India Department of Applied Data Science Noroff University College Oslo Norway Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of CS SEECS NUST Islamabad Pakistan National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Communication Engineering National University of Science and Technology Muscat Sultanate of Oman

A serious, all-encompassing, and deadly cancer that affects every part of the body is skin cancer. The most prevalent causes of skin lesions are UV radiation, which can damage human skin, and moles. If skin cancer is discovered early, it may be adequately treated. In order to diagnose skin lesions with less effort, dermatologists are increasingly turning to machine learning (ML) techniques and computer-aided diagnostic (CAD) systems. This paper proposes a computerized method for multiclass lesion classification using a fusion of optimal deep-learning model features. The dataset used in this work, ISIC2018, is imbalanced; therefore, augmentation is performed based on a few mathematical operations. After that, two pre-trained deep learning models (DarkNet-19 and MobileNet-V2) have been fine-tuned and trained on the selected dataset. After training, features are extracted from the average pool layer and optimized using a hybrid firefly optimization technique. The selected features are fused in two ways: (i) original serial approach and (ii) proposed threshold approach. Machine learning classifiers are used to classify the chosen features at the end. Using the ISIC2018 dataset, the experimental procedure produced an accuracy of 89.0%. Whereas, 87.34, 87.57, and 87.45 are sensitivity, precision, and F1 score respectively. At the end, comparison is also conducted with recent techniques, and it shows the proposed method shows improved accuracy along with other performance measures.

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Empowering Oral Healthcare: CNN-Assisted Diagnosis for Improved Patient Outcomes

Empowering Oral Healthcare: CNN-Assisted Diagnosis for Impro...

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data science and Information System (ICDSIS), IEEE International Conference on

作者： Vaishnavi Garrepalli Vishwanath Gupta S Abhishek T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Verizon Data Services India pvt Ltd Hyderabad India Rolls-Royce Inc Bangalore India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

In today’s world, health is very important, and among the many aspects of well-being, oral wellness is one of the crucial components. Diagnostic investigation in everyday clinical procedures primarily consists of reviewing periapical radiography, which visualizes dental anatomy such as teeth, bone, and soft tissues within the oral cavity. Nonetheless, subjective errors may arise during human interpretation of dental radiographs which also take a lot of time. Most of these dental X-rays have an important role in diagnosing various conditions like Implant, Fillings, Cavity, and Impacted teeth under accurate assessment of classification of dental radiographs. These imaging techniques can help dentists detect anomalies within the mouth through this diagnosis process. To improve the accuracy of diagnosis and reduce the costs of clinical operations, this study presents a novel approach that automates the detection of problematic teeth and other dental diseases using these x-ray images using CNN (Convolutional Neural Networks) architecture VGG19 for making the right diagnosis choices.

关键词： Accuracy Computational modeling Teeth X-rays Feature extraction Dentistry Convolutional neural networks

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Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Large Knowledge Graphs with Applications in Drug Repurposing

Explainable Enrichment-Driven GrAph Reasoner (EDGAR) for Lar...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Olasunkanmi, Olawumi Morris, Evan Kebede, Yaphet Lee, Harlin Ahalt, Stanley C. Tropsha, Alexander Bizon, Chris University of North Carolina at Chapel Hill Department of Computer Science Chapel HillNC United States University of North Carolina at Chapel Hill Renaissance Computing Institute Chapel HillNC United States University of North Carolina at Chapel Hill School of Data Science and Society Chapel HillNC United States University of North Carolina at Chapel Hill Division of Chemical Biology and Medicinal Chemistry Unc Eshelman School of Pharmacy Chapel HillNC United States

ISBN: (纸本)9798350362480

Knowledge graphs (KGs) represent the connections and relationships between real-world entities. We propose a link prediction framework on KGs named Enrichment-Driven GrAph Reasoner (EDGAR) that infers new edges by mining entity-local rules. This approach is based on enrichment analysis, a well-established statistical method used to calculate mechanisms common to a set of differentially expressed genes. EDGAR's inference results are inherently explainable and rankable, equipped with p-values for statistical significance of each enrichment-based rule. We demonstrate its effectiveness on a large-scale biomedical KG, ROBOKOP, focusing on drug repurposing for Alzheimer disease (AD) as a case study. Initially, we extracted 14 known drugs from the KG and identified 20 contextual biomarkers through enrichment analysis, shedding light on functional pathways relevant to the shared efficacy of drugs for AD. Subsequently, using the top 1,000 enrichment results, our enrichment-driven system identified 1,246 additional drug candidates for AD treatment. We validated the top 10 candidates using medical literature evidence. EDGAR is deployed within ROBOKOP, along with a web user interface. This is the first work to use enrichment analysis for either large graph completion or drug repurposing. © 2024 IEEE.

关键词： Knowledge graph

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Improving the Computational Efficiency of Adaptive Audits of IRV Elections

arXiv

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arXiv 2024年

作者： Ek, Alexander Blom, Michelle Stark, Philip B. Stuckey, Peter J. Vukcevic, Damjan Department of Econometrics and Business Statistics Monash University Clayton Australia Department of Computing and Information Systems University of Melboure Parkville Australia Department of Statistics University of California BerkeleyCA United States Department of Data Science and AI Monash University Clayton Australia

AWAIRE is one of two extant methods for conducting risk-limiting audits of instant-runoff voting (IRV) elections. In principle AWAIRE can audit IRV contests with any number of candidates, but the original implementation incurred memory and computation costs that grew superexponentially with the number of candidates. This paper improves the algorithmic implementation of AWAIRE in three ways that make it practical to audit IRV contests with 55 candidates, compared to the previous 6 candidates. First, rather than trying from the start to rule out all candidate elimination orders that produce a different winner, the algorithm starts by considering only the final round, testing statistically whether each candidate could have won that round. For those candidates who cannot be ruled out at that stage, it expands to consider earlier and earlier rounds until either it provides strong evidence that the reported winner really won or a full hand count is conducted, revealing who really won. Second, it tests a richer collection of conditions, some of which can rule out many elimination orders at once. Third, it exploits relationships among those conditions, allowing it to abandon testing those that are unlikely to help. We provide real-world examples with up to 36 candidates and synthetic examples with up to 55 candidates, showing how audit sample size depends on the margins and on the tuning parameters. An open-source Python implementation is publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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A Novel Design of Smart and Intelligent Soldier Supportive Wireless Robot for Military Operations 2

A Novel Design of Smart and Intelligent Soldier Supportive W...

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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Gnanaprakasam, C. Swarna, M. Geetha, R. Saranya, G. Shakthi Murugan, K.H. Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai600123 India Dr. M.G.R. Educational and Research Institute University Department of Electronics and Communication Engineering Tamilnadu Chennai600095 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Kuniyamuthur Coimbatore641008 India Narayana Engineering College Department of Electronics and Communication Engineering Andhra Pradesh Gudur524101 India

ISBN: (纸本)9798350315905

There is an increasing desire for the deployment of military robots in today's politically unstable globe to help soldiers complete dangerous operations. The Military Rescue Robot is well-suited for performing hazardous duties including bomb disposal, observation of hostile territory, and search and rescue operations. The military and law enforcement are finding more and more uses for robots and their ancillary systems as these systems grow more technically advanced, dependable, and compact. Armed forces are increasingly relying on mobile robots for tasks like patrolling and defusing bombs. Many apps available online make use of the Bluetooth, Wi-Fi, and ZigBee technologies already present in these mobile phones in order to facilitate the remote management of additional gadgets. The livestream from the camera module and the data analysis of environmental composition by various sensors of the area under observation may help the military best prepare for missions. Arduino and Zigbee are advanced devices that might be used to achieve the aforementioned *** Zigbee is linked to the Arduino mega, which in turn is linked to the various sensors and the robotic arm. Zigbee technology is used for data transmission and reception. © 2023 IEEE.

关键词： Military operations

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Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2023年第3期42卷 1000-1014页

作者： Shao, Hao-Chiang Ping, Hsing-Lei Chen, Kuo-Shiuan Su, Weng-Tai Lin, Chia-Wen Fang, Shao-Yun Tsai, Pin-Yian Liu, Yan-Hsiu Institute of Data Science and Information Computing National Chung Hsing University Taichung402 Taiwan National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Phison Electronics Corporation Miaoli350 Taiwan Faraday Technology Corporation Hsinchu300 Taiwan Industrial Technology Research Institute Electronic and Optoelectronic System Research Laboratories Hsinchu310 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan United Microelectronics Corporation Product Engineering Department Hsinchu300 Taiwan United Microelectronics Corporation Development of Smart Manufacturing Hsinchu300 Taiwan

Learning-based presimulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circuit. Such models are usually driven by pairwise learning, involving a training set of layout patterns and their reference shape images after fabrication. However, it is expensive and time consuming to collect the reference shape images of all layout clips for model training and updating. To address the problem, we propose a deep-learning-based layout novelty detection scheme to identify novel (unseen) layout patterns, which cannot be well predicted by a pretrained presimulation model. We devise a global-local novelty scoring mechanism to assess the potential novelty of a layout by exploiting two subnetworks: 1) an autoencoder and 2) a pretrained presimulation model. The former characterizes the global structural dissimilarity between a given layout and training samples, whereas the latter extracts a latent code representing the fabrication-induced local deformation. By integrating the global dissimilarity with the local deformation boosted by a self-attention mechanism, our model can accurately detect novelties without the ground-truth circuit shapes of test samples. Based on the detected novelties, we further propose two active-learning strategies to sample a reduced amount of representative layouts most worthy to be fabricated for acquiring their ground-truth circuit shapes. Experimental results demonstrate: 1) the effectiveness of our layout novelty detection algorithm and 2) the ability of our active-learning strategies in selecting representative novel layouts for keeping a learning-based presimulation model updated. © 1982-2012 IEEE.

关键词： Fabrication

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Forte: An Interactive Visual Analytic Tool for Trust-Augment...

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Innovative Smart Grid Technologies (ISGT)

作者： Kaustav Bhattacharjee Soumya Kundu Indrasis Chakraborty Aritra Dasgupta Department of Data Science New Jersey Institute of Technology USA Optimization and Control Group Pacific Northwest National Laboratory USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

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A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks

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Acta Biomaterialia 2025年 200卷 283-298页

作者： Wu, Wensi Daneker, Mitchell Herz, Christian Dewey, Hannah Weiss, Jeffrey A. Pouch, Alison M. Lu, Lu Jolley, Matthew A. Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Philadelphia PA United States Cardiovascular Institute Children's Hospital of Philadelphia PhiladelphiaPA United States Department of Anesthesiology and Critical Care Medicine Children's Hospital of Philadelphia PhiladelphiaPA United States Division of Cardiology Children's Hospital of Philadelphia PhiladelphiaPA United States Department of Statistics and Data Science Yale University New HavenCT United States Department of Chemical and Biochemical Engineering University of Pennsylvania PhiladelphiaPA United States Department of Biomedical Engineering University of Utah Salt Lake CityUT United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Pennsylvania PhiladelphiaPA United States

Computer simulation of "virtual interventions" may inform optimal valve repair for a given patient prior to intervention. However, the paucity of noninvasive methods to determine in vivo mechanical parameters of valves limits the accuracy of computer prediction and their clinical application. To address this, we propose a noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks. In this work, we demonstrated its application to the tricuspid valve of a child. We first tracked valve displacements from open to closed frames within a 3D echocardiogram time sequence using image registration. Physics-informed neural networks were subsequently applied to estimate the nonlinear mechanical properties from first principles and reference displacements. The simulated model using these patient-specific parameters closely aligned with the reference image segmentation, achieving a mean symmetric distance of less than 1 mm. Our approach doubled the accuracy of the simulated model compared to the generic parameters reported in the literature. © 2025 Acta Materialia Inc.

关键词： Noninvasive medical procedures

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS

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Procedia Computer science 2025年 252卷 365-380页

作者： Senthil G. A S.U. Suganthi L. Prinslin R. Selvi R. Prabha Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Assistant Professor Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative AI Deep Learning Agriculture Smart Farming Crop Health Yield Prediction Global Positioning System Geographic Information System Artificial Neural Networks Generative Adversarial Networks Sustainability You Only Look Once (YOLO)

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