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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Akl, James Kodate, Shreedhar Calli, Berk Worcester Polytechnic Institute Robotics Engineering Department WorcesterMA01609 United States Worcester Polytechnic Institute Data Science Program WorcesterMA01609 United States

ISBN: (纸本)9798350320695

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our 'Oxy-fuel Cutting Task State' (OCTS) dataset is available at ***/10.5281/zenodo.7734951. © 2023 IEEE.

关键词： Risk assessment

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Study of bioaerosol disinfection kinetics and application of nonlinear regression modeling for optimization of TiO2-based photocatalytic disinfection process

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Nanotechnology for Environmental engineering 2023年第4期8卷 911-922页

作者： Nishad, Vartika Mandal, Chinmoy Sahu, Manoranjan Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay 507 Powai Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

Bacteria and viruses are some of the major sources of indoor air pollution. Many strategies are utilized to control indoor biopollutants. Among the new emerging technologies, this work focuses on the photocatalytic oxidation process and application of disinfection models to optimize the photocatalytic process in bioaerosol control. The kinetics of bioaerosol disinfection was investigated in the photocatalytic process with nonlinear regression analysis for Serratia marcescens and E. coli bacteria. The Chick–Watson model was found suitable and was applied for determination of the inactivation kinetics. The inactivation rate constant k’ was found to be 0.0038 min−1 for S. marcescens and 0.09 min−1 for E. coli. The outcomes showed that in the process of disinfection kinetics of bioaerosols, pseudo-first-order kinetics fits well. With nonlinear regression analysis, a model was developed based on functional parameters such as UV intensity (20 to 100 W/m2), relative humidity (30 to 85%) and air velocity (37 to 112 ft/min) for S. marcescens and TiO2 loading (960 and 1516 mg/m2), UV intensity (0.5 to 3.4 mW/cm2) and relative humidity (51 to 85%) for E. coli. For E. coli overall inactivation was found to increase with an increase in TiO2 loading, RH or UV intensities. The inactivation rate of S. marcescens increased with an increase in UV intensity;however, it was found to decrease at very high or low values of relative humidity and air velocity. Hence, the optimum values for relative velocity were found to be 50% and air velocity of 74 ft/min for S. marcescens. The rate expressions derived for both data can be utilized for predicting and optimizing the disinfection rate at different conditions. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Titanium dioxide

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Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach 3rd

Classification and Prediction of Knee Osteoarthritis by Deep...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Saraswat, Amit Tooley, Tanya Shrivastav, Snigdha Srivastava, Devesh Kumar Department of Electrical Engineering Manipal University Rajasthan Jaipur303007 India Senior Analyst EY Haryana Gurgaon India Master of Science in Data Science program at the University of Rochester New York United States Department of Information Technology Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

The purpose of this research work on medical image analysis is to identify and diagnose knee osteoarthritis (OA) utilizing medical images by applying machine learning techniques. Knee OA is a degenerative joint disease that is becoming more prevalent in aged people worldwide. The study examines the effectiveness of several machine learning algorithms, including deep learning, to analyze medical images of knee OA, such as X-rays and magnetic resonance imaging (MRI). The research findings suggest that using machine learning algorithms might greatly improve the accuracy and efficacy of detecting knee OA, especially in its early stages. Moreover, machine learning algorithms can assist in the creation of specialized treatment plans by predicting the progression of the disease and recommending appropriate remedies. The findings of this study show how machine learning-based medical image analysis has the potential to enhance patient outcomes and quality of life for those with knee OA. Cancer detection diagnosis, neurological condition identification, and cardiovascular function analysis are a few instances of medical picture analysis employing machine learning. Moreover, image-guided treatments have been made more accurate and surgical planning has been made easier with the use of machine learning algorithms. Various methods are applied to achieve a test accuracy of 76.10% which is just 5% less than the accuracy provided by the Kellen-Lawrence Grading Scale. Overall, machine learning-based medical image analysis has the potential to revolutionize medical practice by facilitating earlier and more precise diagnosis, individualized treatment planning, and improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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Impact of spatial distribution of light intensity on disinfection kinetics of Mycobacterium smegmatis and E. coli using TiO2-based photocatalyst

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Nanotechnology for Environmental engineering 2023年第2期8卷 567-579页

作者： Mandal, Chinmoy Nishad, Vartika Sahu, Manoranjan 507 Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Powai Mumbai400076 India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

Photocatalysis using various nanomaterials of different characteristics has been investigated with various metrics for achieving optimal efficiency. However, the spatial distribution of light intensities from the light source, a key factor in photocatalysis process and the selection of an appropriate kinetic model for explaining disinfection characteristics of different microorganisms have not been given ample consideration. In this study, a suitable model has been applied to identify spatial distribution of light based on the light source used and applied in the kinetic models to identify the disinfection rate. Disinfection efficiencies of nanoparticles (TiO2 and Cu-doped TiO2) with different light intensities on gram-positive bacteria, Mycobacterium smegmatis and gram-negative bacteria E. coli was evaluated. The kinetic study revealed that Mycobacterium smegmatis fits well with Chick–Watson model, while E. coli fits well with delayed Chick–Watson model. The rate constants (k) were found to be 0.013 min−1 (TiO2), 0.021 min−1 (3%Cu–TiO2) and 0.020 min−1 (Cu2+) for Mycobacterium and 0.019 min−1 (TiO2) for E. coli. From model evaluation, light intensity decreased exponentially from 3.07 µW cm−2 (3 cm) to 0.98 µW cm−2 (6 cm) and 0.10 µW cm−2 (25 cm) resulting in a sharp decline in the inactivation potential;however, the effect was insignificant beyond 10 cm. On doubling the intensity, disinfection efficiency increased by 1.6 log CFUml−1 (TiO2) and 2.4 log CFU ml−1 (Cu–TiO2), but with one-fourth of intensity it decreased by 1.2 log CFUml−1 (TiO2) and 1.8 log CFUml−1 (Cu–TiO2). Therefore, by optimizing the combination of nanoparticles like TiO2 and Cu, light intensity at 6.14 µW cm−2 and optimal spatial distribution of the light source at 3 cm, increased efficiency of microbial disinfection could be achieved. Thus, this study provides an approach to optimize photocatalytic parameters with inclusion of spatial distribution of light intensity for enhancing the photocatalyt

关键词： Titanium dioxide

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Computer vision

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Swarm Intelligence Path-Planning Pipeline and Algorithms for UAVs: Simulation, Analysis and Recommendation 26

Swarm Intelligence Path-Planning Pipeline and Algorithms for...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Harris, Wyatt Tseng, Sean Viso, Tabatha Weissman, Max Ngan, Chun-Kit Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States

ISBN: (纸本)9789897586927

This research work aims to support domain experts in the selection of proper path planning algorithms for UAVs to solve a domain business problem (i.e., the last mile delivery of goods). In-depth analysis, insight, and recommendations of three promising approaches, including reinforcement learning-based, bio-inspired-based, and physics-based are used to address the multi-agent UAV path planning problem. Specifically, the contributions of this work are fourfold: First, we develop a unified pipeline to implement each approach to conduct this analysis. Second, we build a 2D UAV path planning environment to simulate each approach. Third, using this 2D environment, we run the 450 simulations in three different group sizes of swarm UAV agents (i.e., 3, 5, and 10) within three environments of varying complexity (i.e., Easy, Intermediate, and Hard). We aggregate the simulation data and compare their performance in terms of success rate, run-time, and path length while using the classical A* Search as a baseline. Finally, based upon the performance of each approach and our analytical investigations, we provide informed recommendations for the optimal use case of each UAV path planning approach. The recommendations are presented using parameters for environmental complexity and urgency of goods delivery. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Motion planning

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data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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Detecting Foreground in Videos via Posterior Regularized Robust Bayesian Tensor Factorization 19

Detecting Foreground in Videos via Posterior Regularized Rob...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Xia, Shenghao Zhang, Yinwei Zhang, Biao Liu, Jian University of Arizona Statistics & Data Science Gidp Program Department of Mathematics United States University of Arizona Department of System & Industrial Engineering United States Abb Inc. United States

ISBN: (纸本)9798350320695

Foreground detection is a critical step for separating the moving object from the background in video processing. Tensor factorization has been used in foreground detection due to its ability to process complex high-dimensional data, such as color images and videos. However, traditional tensor factorization often lacks the ability for uncertainty quantification. Bayesian tensor factorization can measure the uncertainty by considering the distributions of the tensor factorization model parameters. Besides, domain knowledge is commonly available and could improve the accuracy of foreground detection of the Bayesian tensor factorization model if it can be appropriately incorporated. In this work, a new Bayesian tensor factorization model, named Posterior Regularized Bayesian Robust Tensor Factorization (PR-BRTF), is proposed with incorporating characteristics of dynamic foreground, as a sparsity posterior regularization term. Furthermore, the variational Bayesian inference and L1 norm is combined for inducing sparsity with an efficient inference. The experiments in real-world case studies have shown the performance improvement of the proposed model over state-of-art methods. © 2023 IEEE.

关键词： Inference engines

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