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International Conference on Business and Industrial Research (ICBIR)

作者： Peerapong Dangrungroj Monton Udompitaksook Natapong Intarasuk Charnchai Pluempitiwiriyawej Teerapol Silawan Department of Electrical Engineering Communications and Information Engineering (CIE) Laboratory Chulalongkorn University Bangkok Thailand Sky Visual Imaging Venture Co. Ltd Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

The Diameter at Breast Height (DBH) measurements are essential for forest management and carbon absorption estimation in environmental challenges. Traditional DBH measurements require more precision and efficiency, especially in inaccessible forests. Existing methods, including manual measurement or remote sensing, struggle with precision in low-intensity point cloud areas. Our technique solves these limitations by analyzing the entire tree stem by the process, including convex hull construction and advanced noise reduction using real-world datasets. It has reduced the Root Mean Squared Error (RMSE) to 0.020, or 92.25% improvement, and the bias has decreased to -0.008. These enhancements highlight the accuracy and reliability of our DBH estimation technique.

关键词： Point cloud compression Adaptation models Convex hulls Accuracy Noise reduction Estimation Forestry Vegetation Breast Remote sensing

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Prediction of mechanical properties of eco-friendly concrete using machine learning algorithms and partial dependence plot analysis

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Smart Construction and Sustainable Cities 2025年第1期3卷 1-21页

作者： Roy, Tonmoy Das, Pobithra Jagirdar, Ravi Shhabat, Mousa Abdullah, Md Shahriar Kashem, Abul Rahman, Raiyan Data Analytics & Information Systems Utah State University Logan USA Department of Civil Engineering Leading University Sylhet Bangladesh Traffic Engineer Fastech Consulting Engineers New Jersey USA Faculty of Engineering Department of Civil Engineering The University of Jordan Amman Jordan Department of Civil and Environmental Engineering Lamar University Beaumont USA Department of Information Science and Library Management University of Rajshahi Rajshahi Bangladesh

Rice husk ash concrete (RHAC) shows promise as a beneficial supplementary material in concrete. However, determining mechanical properties such as compressive strength (CS) and splitting tensile strength (STS) of RHAC through conventional lab-scale methods is laborious and time-consuming. In this research, seven important variables were selected as inputs to predict CS and STS using machine learning (ML) models, including Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) with grid search optimization. The result presented revealed that selected machine learning models provide well accuracy for CS and STS estimates. Among these, the DTR model demonstrated superior performance, with CS prediction R2, RMSE, MAE, and MAPE values of 0.964, 3.314, 2.225, and 5.068, at the testing stage respectively. For STS at the testing stage, DTR achieved R2 of 0.969, RMSE of 0.177, MAE of 0.1322, and MAPE of 3.413. GPR and RFR models also performed well, with R2 values of 0.9434 and 0.9530 for CS prediction. The partial dependence plot (PDP) analysis revealed the optimal mix design parameters for achieving the desired strength. These results offer valuable insights for sustainable construction, allowing engineers to efficiently predict and optimize material performance, reducing the reliance on time-consuming lab methods.

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Structuring a Model to Estimate Human Joint Motion from Surface-Electromyography

Structuring a Model to Estimate Human Joint Motion from Surf...

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Annual Conference of Industrial Electronics Society

作者： Daiki Sodenaga Kosuke Shikata Issei Takeuchi Daswin De Silva Seiichiro Katsura Department of System Design Engineering Keio University Yokohama Japan Abstech. Co. Ltd. Saitama Japan Research Center for Data Analytics and Cognition La Trobe University Melbourne Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In this paper, the structure of a model to estimate human motion from surface-electromyography (sEMG) was defined mathematically and physically. Especially, an infinite impulse response (IIR) filter and an element description method were focused to generate the model in this paper. Human motion can be estimated from sEMG. However, the relationship between sEMG and human motion has a complex characteristics such as a non-linear relationship. The cause of the above complex relationship can be thought the muscle contraction velocity. Then, the IIR filter was applied to consider about the previous sEMG value in the estimation. In addition, some conventional models have been a black-box model and it is not easy to interpret the model mathematically and physically. From the above, an element description method (EDM) was applied to generate the model in this paper. EDM is one of the system identification method and it is easy to interpret the model mathematically and physically because EDM can generate the block diagram of the *** this way, the proposed method could solve two conventional methods about the muscle contraction velocity and the black box model. Then, it was possible to define the structure of the model by the above in this paper. In this paper, the models about a wrist and a fingertip motion were generated and considered about the structure. As the results, the structure of models was defined with a hyperbolic tangent function. It will be possible to apply the model to various motions by just identifying the parameters about the relationship between each joint and muscle because the amount of muscles and range of sEMG are deferent from each muscle.

关键词： Wrist Industrial electronics Accuracy Estimation IIR filters Muscles Mathematical models System identification Response surface methodology Root mean square

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AI-Empowered Peatland Management for Climate-Resilient and Sustainable Future: Challenges and Opportunities

AI-Empowered Peatland Management for Climate-Resilient and S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Saeed Hamood Alsamhi Neil J Rowan Ammar Hawbani Farhan Nashwan Niall O’Brolchain Insight Centre for Data Analytics University of Galway Galway Ireland Centre for Sustainable Disinfection and Sterilization TUS Athlone Ireland Shenyang Aerospace University Shenyang China Faculty of Engineering IBB University Ibb Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Peatlands are crucial for preserving biodiversity, controlling temperatures and storing carbon on a scale. The vital ecological systems face threats from the degradation of land and deforestation, which leads to impacts of climate change, while Artificial Intelligence (AI) plays a vital role in effectively addressing the challenges. By exploring how AI supports monitoring peatlands and climate change, we propose a novel framework that integrates AI and peatland management in order to bolster climate resilience and sustainability efforts based on evidence. Therefore, policymakers can improve the peatland restoration, monitoring and conservation programs by leveraging AI for data analysis, predictive modelling and decision-making processes. AI enables real-time tracking of peatland conditions, detecting activities and wildfires, and optimizing land management strategies. Through the presentation of case studies and best practices, this study underscores how incorporating AI-driven policies for peatland management can strengthen ecosystem sustainability and climate resilience.

关键词： Climate change Wildfires Ecosystems Real-time systems Stakeholders Artificial intelligence Sustainable development

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Deep learning algorithms to classify Fitzpatrick skin types for smartphone based NIRS imaging device 15

Deep learning algorithms to classify Fitzpatrick skin types ...

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Next-Generation Spectroscopic Technologies XV 2023

作者： Leizaola, Daniela Sobhan, Masrur Kaile, Kacie Mondal, Ananda Mohan Godavarty, Anuradha Optical Imaging Laboratory Department of Biomedical Engineering Florida International University 10555 W Flagler St MiamiFL33174 United States Machine Learning and Data Analytics Group Knight Foundation School Computing and Information Sciences Florida International University 11200 SW 8th St MiamiFL33199 United States

ISBN: (数字)9781510661479

ISBN: (纸本)9781510661462

Non-contact imaging modalities for monitoring wound health could supplement the current standard which is a visual inspection by clinicians. Recently, a smartphone oxygenation tool (SPOT) has been developed for physiological imaging of tissue oxygenation changes in response to treatment. However, upon visual inspection of the wound bed and surrounding area, there are variations of pigmentation. Melanin concentration is a highly absorbing chromophore that can impact spatial oxygenation measurements. The objective of this study was to classify the six Fitzpatrick Skin Types (FST) by applying deep learning techniques prior to correcting SPOT’s oxygenation maps. In this IRB-approved study, control subjects were imaged on seven skin locations of varying FST (I-VI) under three different lighting conditions using SPOT device’s camera. A benchmark dataset with samples of 28 × 28 pixel images of human subjects’ feet was developed in three color spaces (R-G-B, Y-Cb-Cr, L-a-b). A deep learning algorithm, specifically a convolutional neural network (CNN), was used to classify skin into six FST (classes). Preliminary results showed the skin types on control subjects’ feet could be classified using deep learning with hyperparameter tuning with accuracies of > 82%. Our ongoing efforts are focused on extensive in-vivo studies on control subjects of FST I-VI on feet towards future implementation of the technology for diabetic wounds and oxygenation mapping using the SPOT device. © 2023 SPIE.

关键词： Smartphones

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Novel Framework for Alzheimer Early Diagnosis using Inductive Transfer Learning Techniques 2

Novel Framework for Alzheimer Early Diagnosis using Inductiv...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Sahu, Himanshu Kumar Kumar, Santosh Alsamhi, Saeed Hamood Chaube, Mithilesh Kumar Curry, Edward Shri Shankaracharya Technical Campus Bhilai India IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Galway Insight Centre for Data Analytics Galway Ireland IBB Univeristy Faculty of Engineering Ibb Yemen IIIT-Naya Raipur Department of Mathematical Sciences Chhattisgarh India

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

The early diagnosis of Alzheimer's disease (AD) is gaining popularity due to massive applications in the medical field. The estimated 50 million people affected by AD worldwide are increasing rapidly, and the condition has an approximately 1 dollar trillion economic cost. This necessitates developing scalable, economical, and reliable methods for detecting Alzheimer's disease. We describe a unique architecture that uses auditory, cognitive, and linguistic aspects to create a multimodal ensemble system. Scores from the Mini-Mental State Examination are used to assess AD severity, which is detected using specialized artificial neural networks with temporal properties. We first test it using the ADReSS challenge dataset, a balanced dataset available through Dementia Bank that contains no biases against any patient. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 93.30% each, as well as a state-of-the-art test Root Mean Squared Error (RMSE) of 4.60, when it comes to the classification of AD. To our knowledge, the algorithm achieves cutting-edge AD classification accuracy of 88.0 % when tested against the benchmark Dementia Bank Pitt database. In order to build a robust inductive transfer learning model, our study highlights the applicability and transferability of spontaneous speech and demonstrates generalizability by utilising a task-agnostic feature space. © 2022 IEEE.

关键词： Classification (of information)

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Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

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Computers, Materials & Continua 2021年第8期68卷 2363-2376页

作者： Muhammad Usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq Al-khawarizmi Institute of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan UniversityRiyadhSaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),*** treatment methods for ADHD are either psycho-stimulant medications or cognitive *** traditional methods,namely therapy,need a large number of visits to hospitals and include *** could be used for the effective treatment of *** could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain *** paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial *** proposed system consists of a puzzle game where its complexity increases with each *** signals were acquired using the Neurosky headset;then sent the signals to the designed gaming *** neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation *** results show that this game improves player overall performance from 74%to 98%by playing each game level.

关键词： Neurogaming brain-computer interface attention deficit hyperactivity disorder electroencephalography spatial memory cognitive skills attention level

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NSMD-NAS: Retinal Image Segmentation with Neural Architecture Search and Non-Subsampled Multiscale Decomposition

NSMD-NAS: Retinal Image Segmentation with Neural Architectur...

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Congress on Evolutionary Computation

作者： Hanyu Zhang Lixin Tang Xiangman Song Te Xu National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shen Yang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shen Yang China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Accurate segmentation of the microvascular component of the retinal fundus image is of great significance for the clinical diagnosis. Currently, deep learning-based methods are often employed for retinal fundus image segmentation. However, most deep learning models manually designed by experts are not suitable for task-specific applications. Therefore, we propose a novel retinal image segmentation method that integrates a neural architecture search framework with a non-subsampled multiscale decomposition (NSMD-NAS) technique. Specifically, the feature extraction layer includes the convolutional layer and the transformer layer. In addition, the non-subsampling filter bank is embedded into the convolutional and the transformer modules respectively to generate a new network module for exploring multiscale, multi-frequency, and multi-directional information. Unlike previous wavelet-based network modules, non-subsampling network modules have translation invariance. Subsequently, the components of the network are specified by the elaborated encoding strategy, and then the network with the highest adaptation is searched by a genetic evolutionary algorithm (GA) to finally obtain the best network structure. Experimental results demonstrate the effectiveness of each component in the proposed model on the public benchmark datasets.

关键词： Convolutional codes Wavelet transforms Image segmentation Filter banks Computer architecture Evolutionary computation Retina

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Handling Imbalanced and Poorly Separated data: a Multi-Stage Multi-Group Machine Learning Approach

Handling Imbalanced and Poorly Separated Data: a Multi-Stage...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lee, Eva K. Yuan, Fan Mann, Barton J. Egan, Brent Center for Operations Research in Medicine and Healthcare The Data and Analytics Innovation Institute Atlanta United States Georgia Institute of Technology Industrial and Systems Engineering Atlanta United States The American Orthopedic Society for Sports Medicine The American Orthopedic Society for Sports Medicine Chicago United States American Medical Association Cardiovascular Health SC United States

ISBN: (纸本)9798350337488

Poorly separated data and imbalanced data present major challenges to classifiers which often result in lower accuracy and reliability in making predictions. In this paper, we introduce the multi-stage classification construct in which 'difficult-to-classify' observations are placed into a reserved judgment region for delayed future classification. Such a design is well-suited for poorly separated data that are difficult to classify without committing a high percentage of misclassification errors. The misclassification constraints within the classifier can be finetuned to allow management of imbalanced data, guiding minority entities into the reserved judgement region to avoid being misclassified into the majority groups. We wrap the classifier with a fast feature selection heuristic based on particle swarm optimization. An exact combinatorial branch-and-bound algorithm is also implemented to measure the quality of the heuristic solutions. We apply this multi-stage multi-group machine learning framework to two real-life medical problems: (a) multi-site treatment outcome prediction for best practice discovery in cardiovascular disease, and (b) uncovering patient characteristics that predict optimal response to intra-articular injections of hyaluronic acid for the treatment of knee osteoarthritis. Both problems involve poorly separated data and imbalanced groups in which traditional classifiers yield low prediction accuracy (47% - 66%). The multi-stage BB-PSO/DAMIP manages the poorly separated and imbalanced data well and returns interpretable results with 82% - 97% blind prediction accuracy. © 2023 IEEE.

关键词： branch-and-bound cardiovascular disease discriminant analysis via mixed integer program imbalanced data knee osteoarthritis machine learning for evidence-based practice multi-site knowledge discovery multi-stage multi-group machine learning framework particle swarm optimization poorly separated data

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Accounting for Individual Shooting Skills in Combat Models 23

Accounting for Individual Shooting Skills in Combat Models

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Proceedings of the Winter Simulation Conference

作者： Vikram Mittal Paul F. Evangelista Department of Systems Engineering Mahan Hall United States Military Academy West Point NY USA Office of Data and Analytics Taylor Hall United States Military Academy West Point NY USA

ISBN: (纸本)9798350369663

There is significant variation in shooting ability among U.S. Army soldiers, which is often overlooked in combat simulations. This study introduces a Monte-Carlo model to estimate the dispersion of a soldier's shot group based on their marksmanship score. This model is used to assess the impact of marksmanship on a squad's performance through two analyses. The first analysis employs a dueling model to examine various marksmanship skills between dueling teams, offering insights into overmatch requirements. The second analysis uses an agent-based combat simulation to investigate the influence of marksmanship on squad performance in a dueling scenario in addition to tactical rural and urban missions. The results reveal that marksmanship becomes increasingly crucial in enhancing lethality and survivability as the distance between combatants grows. Notably, superior marksmanship skills are particularly vital in offensive, rural operations. These findings emphasize the significance of marksmanship and its implications for military requirements and tactical decision-making.

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