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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Razeeth, Suhail Shihara, Fathima Pavithra, Kasuni Kaliyamoorthy, Maran Nawaz, Samsudeen Sabraz Kariyapper, R.K.A.R. South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka Uva Wellassa University of Sri Lanka Dept. of Computer Science and Informatics Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India South Eastern University of Sri Lanka Department of Management and Information Technology Sri Lanka

ISBN: (纸本)9798350394580

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers. © 2023 IEEE.

关键词： crowdsourcing GIS mobile application water availability web application

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Tactile Sensing with Contextually Guided CNNs: A Semisupervised Approach for Texture Classification

Tactile Sensing with Contextually Guided CNNs: A Semisupervi...

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IEEE International Conference on Robotic Computing (IRC)

作者： Olcay Kursun Beiimbet Sarsekeyev Mahdi Hasanzadeh Ahmad Patooghy Oleg V. Favorov Dept. of Computer Science Auburn University at Montgomery Montgomery Alabama Dept. of Computer Science University of Central Arkansas Conway Arkansas Dept. of Computer Systems Technology North Carolina A&T State University Greensboro North Carolina Dept. of Biomedical Engineering University of North Carolina at Chapel Hill Chapel Hill North Carolina

Texture classification plays a crucial role in applications ranging from object recognition and product design to surface exploration. Utilizing deep learning methods with sensors, such as accelerometers, offers a way to identify key surface features without the need to precisely replicate human touch. A Contextually Guided Convolutional Neural Network (CG-CNN) employs contextual guidance by developing auxiliary tasks during its training. These tasks offer implicit, yet rigorous, internal supervision signals. When trained with these subtasks, CG-CNN learns to represent the innate structure and patterns within the data, resulting in robust, transferrable, and local/contextual-neighborhood-preserving domain representations. This paper extends the CG-CNN framework for texture classification by integrating semisupervised learning. Empirical evaluations on the VibTac-12 texture dataset reveal that CG-CNN effectively generalizes to novel and unfamiliar textures, even when trained with scarce labeled examples. By harnessing vast amounts of unlabeled, contextually relevant data alongside the labeled samples, CG-CNN ensures robust and precise texture classification. Such advancements hold promise for applications in robotics, prosthetics, and haptic interfaces.

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Computational Modeling Towards Focused Ultrasound Therapy for Spinal Cord Injury: Visualization of Beam Propagation through Patient-Specific Anatomy

Computational Modeling Towards Focused Ultrasound Therapy fo...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Kumar, Avisha Tsehay, Yohannes Gonzalez, Eduardo Kerensky, Max J. Lediju Bell, Muyinatu A. Theodore, Nicholas Thakor, Nitish V. Manbachi, Amir Dept. of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Johns Hopkins HEPIUS Innovation Laboratory BaltimoreMD21218 United States Dept. of Neurosurgery School of Medicine Johns Hopkins University BaltimoreMD21218 United States Dept. of Biomedical Engineering School of Medicine Johns Hopkins University BaltimoreMD21218 United States Dept. of Computer Science Johns Hopkins University BaltimoreMD21218 United States Dept. of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States Dept. of Anesthesiology and Critical Care Johns Hopkins University School of Medicine Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781510660373

Ultrasound holds promise for use in spinal cord injury cases for both diagnostic and therapeutic purposes. Focused ultrasound applications demand an added threshold of study to ensure the safety and efficacy of the therapy. For optimal treatment outcomes, it is crucial to understand whether relevant structures are being targeting with sufficient energy without damaging neighboring tissue and vasculature. However, it is difficult to predict the expected displacement and pressure profile of the ultrasound wavefront due to challenges with visualizing an acoustic beam in real-time and complex patient-specific anatomy. This challenge is particularly prominent in anatomies with varying medium acoustic properties that cause reflection and distortion of the signal, which is inherent to the composition of the spinal cord and is exacerbated by the formations of injury-induced hematomas. Incorrect placement of focused ultrasound transducers can be detrimental to patient health, specifically if therapeutic ultrasound is used at higher intensities, as the beam propagation can target healthy tissue and important structures that could lead to tissue damage and death. We study how computational tools can be leveraged to aid placement of the transducer using an ultrasound simulation software, Wave 3000 Plus, that allows for the visualization of ultrasound propagation through anatomical structures. By simulating the propagation of ultrasound beams through patient-specific Digital Imaging and Communications in Medicine (DICOM) images, we study computational approaches to determine the optimal placement of devices. In this study, we use in vivo porcine spinal cord images following spinal cord injury (as an example medical use case) to determine if the injury site is being targeted appropriately and to visualize the distribution of pressure throughout the simulation. We demonstrate that Wave 3000 Plus is a viable approach for visualizing ultrasound propagation through patient-specific a

关键词： Visualization

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Deep Angiogram: Trivializing Retinal Vessel Segmentation

arXiv

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arXiv 2023年

作者： Hu, Dewei Yao, Xing Wang, Jiacheng Tao, Yuankai K. Oguz, Ipek Vanderbilt University Dept. of Electrical and Computer Engineering NashvilleTN United States Vanderbilt University Dept. of Computer Science NashvilleTN United States Vanderbilt University Dept. of Biomedical Engineering NashvilleTN United States

Among the research efforts to segment the retinal vasculature from fundus images, deep learning models consistently achieve superior performance. However, this data-driven approach is very sensitive to domain shifts. For fundus images, such data distribution changes can easily be caused by variations in illumination conditions as well as the presence of disease-related features such as hemorrhages and drusen. Since the source domain may not include all possible types of pathological cases, a model that can robustly recognize vessels on unseen domains is desirable but remains elusive, despite many proposed segmentation networks of ever-increasing complexity. In this work, we propose a contrastive variational auto-encoder that can filter out irrelevant features and synthesize a latent image, named deep angiogram, representing only the retinal vessels. Then segmentation can be readily accomplished by thresholding the deep angiogram. The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features. Compared to baseline deep segmentation networks, our model achieves higher segmentation performance via simple thresholding. Our experiments show that the model can generate stable angiograms on different target domains, providing excellent visualization of vessels and a non-invasive, safe alternative to fluorescein angiography. © 2023, CC BY.

关键词： Image enhancement

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Attaching Microlearning to Existing Habits

Attaching Microlearning to Existing Habits

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable computers

作者： Takumi Nakai Andrew Vargo Koichi Kise Dept. of Computer Science and Intelligent Systems Osaka Prefecture University Japan Graduate School of Informatics Osaka Metropolitan University Japan

ISBN: (纸本)9781450394239

To take advantage of microlearning, it is desirable for learners to make microlearning a habit. Most of the conventional methods for making microlearning a habit are intended to form a habit from scratch, but attempts to form a habit from scratch require a lot of motivation and workload from the learner. In this study, we propose habit-attached microlearning as a method to make microlearning a habit with less burden. This method designs a mechanism in which a microlearning task is added to the learner’s existing habits. In this study, we focused on web browsing on smartphones as a habit to attach to learning, and developed a web browsing application for learning English vocabulary. This paper reports on a controlled experiment conducted to evaluate the learning and habit-forming effects of the developed application.

关键词： Vocabulary building Web browsing Microlearning Habituation

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Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering

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SN computer science 2022年第3期3卷 184页

作者： Varde, Aparna S. Pandey, Abidha Du, Xu Dept. of Computer Science Environmental Science and Management PhD Program Montclair State University Montclair NJ United States Visiting Researcher at Max Planck Institute for Informatics Saarbrücken Germany Department of Computer Science Montclair State University Montclair NJ United States Department of Earth and Environmental Science Environmental Science and Management PhD Montclair State University Montclair NJ United States

This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine particle air pollutants of diameter less than 2.5 µm (particulate matter 2.5), their concentration being substantially influenced by urban traffic. We collect worldwide multicity data from health-related public sources on which mining is performed using classical data mining/machine learning paradigms: association rules, clustering and classification. Challenges include adapting appropriate techniques based on data, and capturing subtle domain-specific aspects. The prediction tool is built using knowledge discovered by mining, leveraging health standards, catering to novice, intermediate and expert users. The prediction output is accurate, efficient, interpretable and useful as evident from our experiments. The tool is helpful for urban decision support. This work is beneficial in developing software systems such as intelligent tutors, mobile device apps and smart city tools. It contributes to smart environment, mobility and living, making a positive impact on smart cities and sustainability. In this work, we claim that classical computational paradigms in their fundamental form can be adapted to solve environmental engineering problems, with easy comprehension, as per the Occam’s razor principle that advocates simplicity. This article constitutes applied research: using computational techniques to solve domain-specific problems. Future work includes exploring models in deep learning such as CNN and Bi-LSTM, and considering different types of pollutants as well as other sources besides multicity traffic data, to conduct further studies. This would address additional challenges with enhancements. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Applied research Decision support Environmental engineering Health informatics Machine learning Urban studies

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NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images

arXiv

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arXiv 2023年

作者： Vadori, Valentina Peruffo, Antonella Graïc, Jean-Marie Finos, Livio Corain, Livio Grisan, Enrico Dept. of Computer Science & Informatics London South Bank University United Kingdom Dept. of Comparative Biomedicine & Food Science University of Padova Italy Dept. of Developmental Psychology and Socialisation University of Padova Italy Dept. of Management and Engineering University of Padova Italy

Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells. © 2023, CC BY-NC-ND.

关键词： Cells

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model

The Integration of 6G and Blockchain into an Efficient AIoT-...

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IEEE World Symposium on Communication Engineering (WSCE)

作者： Anna Lioupa Vasileios A. Memos Christos L. Stergiou Yutaka Ishibashi Konstantinos E. Psannis Dept. of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece Dept. of Computer Science Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication.

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Graph Neural Networks for HD EMG-based Movement Intention Recognition: An Initial Investigation

Graph Neural Networks for HD EMG-based Movement Intention Re...

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2022 IEEE International Conference on Recent Advances in Systems science and Engineering, RASSE 2022

作者： Massa, Silvia Maria Riboni, Daniele Nazarpour, Kianoush University of Cagliari Dept. of Mathematics and Computer Science Via Ospedale 72 Cagliari09124 Italy University of Edinburgh School of Informatics EdinburghEH8 9AB United Kingdom

ISBN: (数字)9781665494915

ISBN: (纸本)9781665494915

Recently, high-density (HD) EMG electrodes have been proposed for improving amputees' movement/grasping intention recognition, exploiting different machine learning techniques. HD EMG electrodes are composed of a large number of closely spaced channels that simultaneously acquire EMG signals from different parts of the muscle. Given the topological properties of these devices, it is important to fully exploit the spatiotemporal information provided by the electrodes to optimize recognition accuracy. In this work, we introduce the use of Graph Neural Networks (GNNs) to process HD EMG data for movement intention recognition of people with an amputation affecting the upper limbs and which use a robotic prosthesis. In this initial investigation of the approach, we conducted experiments using a real-world dataset consisting of EMG signals collected from 20 volunteers while performing 65 different gestures. We were able to detect 45 gestures with a classification error rate of less than 10%, and obtained an overall classification error rate of 8.75% with a standard deviation of 4.9. To the best of our knowledge, this is the first work in which GNNs are used for processing HD EMG data. © 2022 IEEE.

关键词： Topology

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Recurrent 3-D Multi-Level Visual Transformer For Joint Classification of Heterogeneous 2-d AND 3-D Radiographic Data

Recurrent 3-D Multi-Level Visual Transformer For Joint Class...

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IEEE International Conference on Image Processing

作者： Muhammad Owais Muhammad Zubair Taimur Hassan Divya Velayudhan Irfan Hussain Naoufel Werghi Dept. of Mechanical and Nuclear Engineering KUCARSDept Khalifa University UAE Faculty of IT & Computer Science University of Central Punjab Pakistan Dept.of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE Dept.of Computer Science C2PS Khalifa University UAE

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection. To overcome these challenges, we propose the Recurrent 3-D Multi-level Vision Transformer (R3DM-ViT) model, capable of handling multimodal data to enhance diagnostic accuracy. Our quantitative evaluations demonstrate that R3DM-ViT surpasses existing methods, achieving an impressive accuracy of $96.67 \%$, F1-score of $96.88 \%$, mean average precision of $96.75 \%$, and mean average recall of $97.02 \%$. This research signifies a significant stride forward in the automated detection of lung infections through multimodal imaging.

关键词： Visualization Solid modeling Accuracy Image processing Image retrieval Lung Manuals

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