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3rd IEEE World Conference on applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Sahu, Dinesh Prasad Kumar, Ashwini Ahmad, Salman Khursheed Majumder, Jayeeta Gangopadhyay, Sourav Bennett University School of Computer Science Engineering & Technology Greater Noida India Camellia Institute of Technology & Management Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology & Management Dept. of Applied Computational Science and Engineering Greater Noida India Brainware University Department of Hospital Management Kolkata India

ISBN: (纸本)9798350384598

This study investigates the optimization of healthcare accessibility and effectiveness by integrating artificial intelligence (AI) solutions within Internet of Things (IoT) systems for remote patient monitoring. Embracing a holistic approach, we deploy medical sensors, including temperature, pressure, heart rate, and oxygen sensors, to collect patients' continuous health metrics (vital signs). These sensed data are then transmitted to an Arduino controller, responsible for managing data communication with the cloud infrastructure using a Wi-Fi module that facilitates data storage and analysis. Stored data are accessed by authorized healthcare professionals, who exploit machine learning models, including Artificial Neural Networks (ANN), Logistic Regression (LR) and Decision Tree (DT), to forecast patients' health outcome in advance using the sensed data. A rigorous evaluation demonstrated the IoT system's effectiveness, based on testing with sensed data from ten patients over a month, where 70% of the data were used for training the models, and the rest for testing. The proposed IoT system demonstrates the success;ANN exhibited the highest accuracy at 97.88%, followed by LR at 93.43%, and DT at 91.20%. Precision, recall, and F1 scores further validated the models' ability to predict health outcome accurately. Evaluation of the accuracy and loss curves showed the convergence of the models towards optimal performance. This study contributes to remote healthcare monitoring and provides a promising IoT paradigm for realizing ubiquitous remote monitoring, optimized healthcare effectiveness, and patient outcome through AI-enabled IoT systems. © 2024 IEEE.

关键词： Remote patient monitoring

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Identifying COVID-19 through X-ray and CT scan images using Machine Learning 18

Identifying COVID-19 through X-ray and CT scan images using ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Shehzadi, Maham Arshad, Usama Abideen, Zainul Ali, Raja Hashim Khan, Ammad Aslam Ijaz, Ali Zeeshan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Virtual University of Pakistan Dept. of Biological Sciences Rawalpindi Pakistan National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the current global scenario, marked by the COVID-19 pandemic, it has become imperative for healthcare systems around the globe to swiftly and accurately diagnose the disease. This is where cutting-edge approaches such as machine learning (ML) come into play, specifically, ML-based identification of COVID-19 from chest X-ray and CT-Scan imagery. Our research collective is devotedly working towards devising a robust, accurate ML model capable of deciphering these images to identify COVID-19 effectively. This involves the application of a variety of AI algorithms and vast data resources. Our work is grounded on a large, annotated image dataset that aids in training and refining the model. Our findings indicate that ML methodologies can discern COVID-19 from chest X-ray and CT scan images with a mean accuracy of 90%. Such a tool holds promise in aiding medical practitioners in prompt and precise patient diagnosis, thereby improving patient prognosis. The most notable contribution of our study lies in the creation of an ML model that can accurately detect COVID-19 in chest X-ray and CT-Scan images, potentially aiding in early disease detection and management. © 2023 IEEE.

关键词： COVID-19

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Splicing ViT Features for Semantic Appearance Transfer

arXiv

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arXiv 2022年

作者： Tumanyan, Narek Bar-Tal, Omer Bagon, Shai Dekel, Tali Dept. of Computer Science and Applied Math The Weizmann Inst. of Science

We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image. Our method works by training a generator given only a single structure/appearance image pair as input. To integrate semantic information into our framework-a pivotal component in tackling this task-our key idea is to leverage a pre-trained and fixed Vision Transformer (ViT) model which serves as an external semantic prior. Specifically, we derive novel representations of structure and appearance extracted from deep ViT features, untwisting them from the learned self-attention modules. We then establish an objective function that splices the desired structure and appearance representations, interweaving them together in the space of ViT features. Our framework, which we term "Splice", does not involve adversarial training, nor does it require any additional input information such as semantic segmentation or correspondences, and can generate high resolution results, e.g., work in HD. We demonstrate high quality results on a variety of in-the-wild image pairs, under significant variations in the number of objects, their pose and appearance. © 2022, CC BY.

关键词： Semantics

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Combining Internal and External Constraints for Unrolling Shutter in Videos

arXiv

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arXiv 2022年

作者： Naor, Eyal Antebi, Itai Bagon, Shai Irani, Michal Dept. of Computer Science and Applied Math The Weizmann Institute of Science Israel

Videos obtained by rolling-shutter (RS) cameras result in spatially-distorted frames. These distortions become significant under fast camera/scene motions. Undoing effects of RS is sometimes addressed as a spatial problem, where objects need to be rectified/displaced in order to generate their correct global shutter (GS) frame. However, the cause of the RS effect is inherently temporal, not spatial. In this paper we propose a space-time solution to the RS problem. We observe that despite the severe differences between their xy frames, a RS video and its corresponding GS video tend to share the exact same xt slices - up to a known sub-frame temporal shift. Moreover, they share the same distribution of small 2D xt-patches, despite the strong temporal aliasing within each video. This allows to constrain the GS output video using video-specific constraints imposed by the RS input video. Our algorithm is composed of 3 main components: (i) Dense temporal upsampling between consecutive RS frames using an off-the-shelf method, (which was trained on regular video sequences), from which we extract GS "proposals". (ii) Learning to correctly merge an ensemble of such GS "proposals" using a dedicated MergeNet. (iii) A video-specific zero-shot optimization which imposes the similarity of xt-patches between the GS output video and the RS input video. Our method obtains state-of-the-art results on benchmark datasets, both numerically and visually, despite being trained on a small synthetic RS/GS dataset. Moreover, it generalizes well to new complex RS videos with motion types outside the distribution of the training set (e.g., complex non-rigid motions) - videos which competing methods trained on much more data cannot handle well. We attribute these generalization capabilities to the combination of external and internal constraints. © 2022, CC BY-NC-ND.

关键词： Cameras

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach

Computational Web Drug Discovery Application forSARS- Cov-2 ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Hemkiran S R Kartik S Rathan Aswath K Kabilan Sudha Sadasivam G Dept. of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Dept. of Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly.

关键词： COVID-19 Proteins Machine learning algorithms Biological system modeling Computational modeling Predictive models Prediction algorithms Drug discovery Compounds Random forests

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Pulling Figure of Dielectric Resonator for 3D Filament Defectoscopy 34

Pulling Figure of Dielectric Resonator for 3D Filament Defec...

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34th International Scientific Symposium Metrology and Metrology Assurance, MMA 2024

作者： Miroshnyk, Maryna Zaichenko, Olga Miroshnyk, Anatolii Hapon, Nataliia Pakhomov, Yurii Klymenko, Liubov V. N. Karazin Kharkiv National University Dept. of Theoretical and Applied System Engineering Kharkov Ukraine Kharkiv National University of Radioelectronics Dept. of Design and Operation of Electronic Devices Kharkiv Ukraine Kharkiv National University of Radioelectronics Dept. of Design Automation Kharkiv Ukraine O.M. Beketov National University of Urban Economy in Kharkiv Dept. of Computer Science and Information Technologies Kharkiv Ukraine Ukrainian State University of Railway Transport Dept. of Specialized Computer Systems Kharkiv Ukraine

ISBN: (纸本)9798350352832

The research aim is to derive connection between the parameters of dielectric resonators frequency shift and the warp defect of 3D printing filament. Object of investigation are the processes of interaction of electromagnetic wave of a dielectric resonator with filament for 3D printing. The proposed model describes the connection between the frequency shift and defect warp of filament frequency pulling depending on the load. © 2024 IEEE.

关键词： Q factor measurement

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Virtual Field Trip of the Central Heating and Chiller Plant as an Instructional Tool for Thermodynamics Education 53

Virtual Field Trip of the Central Heating and Chiller Plant ...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Ha, Oai Berrier, Seth Oss, Keif University of Wisconsin-Stout Dept. of Engineering and Technology MenomonieWI United States Dept. of Math Stats and Computer Science University of Wisconsin-Stout MenomonieWI United States University of Wisconsin-Stout Dept. of Design MenomonieWI United States

ISBN: (纸本)9798350336429

This work in progress introduces a framework for developing a virtual tour of the central heating and chiller plant (the Plant) at a university in the US Midwest for teaching and learning Thermodynamics at the campus. Traditionally, this theory-heavy and abstract course is taught using textbooks, lectures, problem solving, and some laboratory experiments. The majority of course content lacks the tangible connections between engineering devices and systems in a close real-world application, making the course challenging and uninteresting for many students. In this project, 360-degree panoramic photos and proper authoring software are employed to render an immersive virtual environment of the Plant that students can visit at any time and from anywhere. The virtual field trip would provide students with tangible insights into thermodynamic and heat transfer processes from over 25 main engineering equipment, devices, and systems at the Plant. Among them are boilers, chillers, a steam turbine, heat exchangers, cooling towers, blowers, and economizers. The follow-up quizzes and assessments are designed to contribute to the course learning outcomes, such as an understanding of phase transitions, thermodynamic properties of steam, and thermal efficiencies of engineering devices. The virtual immersive experiences can help students enhance their learning experiences and engagement in the course's materials without the costs and dangers inherent in a physical tour of the Plant facility. A preliminary result of the virtual tour's evaluation with a group of students and faculty is also included to provide inputs to the next phase of the project. © 2023 IEEE.

关键词： 360-degree panoramic images immersive education mechanical engineering education Thermodynamics education virtual field trip

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A Framework for Human Motion Estimation using IMUs in Human-Robot Interaction

A Framework for Human Motion Estimation using IMUs in Human-...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Ates, Gizem Stolen, Martin Fodstad Kyrkjebo, Erik Western Norway University of Applied Sciences Dept. of Computer Science Electrical Engineering and Mathematical Sciences Forde Norway

ISBN: (纸本)9781728119489

The field of human-robot interaction (HRI) covers a wide range of interactions between humans and robots in various settings. In most cases, both the human and the robot should know the status of each other and interchange data accordingly. Particularly, informing the robot about the human poses and gestures is critical for safe and reliable interaction. We present an open-source inertial measurement unit (IMU)-based human motion estimation framework designed for HRI applications which can be used with multiple robotic systems. The presented framework is developed as a Robot Operating System (ROS) package and takes on a bridge role between the human and the robot by utilizing the estimated human motions to produce a desired robot goal pose. Although there are different human motion tracking systems that are used in gaming, film making, industrial and medical applications today, they are either quite costly, tedious to set up or system dependent. Also, those systems have some limitations to be used in industrial environments such as suffering from occlusion and obstacles between the tracker system and the human, sensitive to light changes etc., and require long calibration steps before each use. The availability of a versatile human motion estimation framework that can be easily used in different HRI scenarios is handy for industrial applications. Some examples of usages of the proposed framework both in simulation and real-world applications are demonstrated in this paper. We aim our package to be a useful boosting tool to develop IMU-based human motion estimated HRI applications and auxiliary templates to develop more complex HRI scenarios for research and development purposes. © 2022 IEEE.

关键词： Human robot interaction

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RadioModRec-l: A Large Scale Radio Frequency Dataset for Automatic Modulation Recognition Research 4

RadioModRec-l: A Large Scale Radio Frequency Dataset for Aut...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adetiba, Emmanuel Olasina, Jamiu R. Obiyemi, Obiseye Covenant University Covenant Applied Informatics and Communication Africa Center of Excellence Dept. of Electrical and Information Engineering Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban4001 South Africa Federal Polytechnic Ilaro Dept. of Computer Engineering Ilaro Nigeria Durban University of Technology Space Science Centre Dept. of Electrical Power Engineering Durban South Africa

ISBN: (纸本)9798350353266

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain. © 2024 IEEE.

关键词： Signal to noise ratio

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Automatically Learning HTN Methods from Landmarks 37

Automatically Learning HTN Methods from Landmarks

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37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Li, Ruoxi Nau, Dana Roberts, Mark Fine-Morris, Morgan Dept. of Computer Science Institute for Systems Research Univ. of Maryland College ParkMD United States Navy Center for Applied Research in AI Naval Research Laboratory WashingtonDC United States

Hierarchical Task Network (HTN) planning usually requires a domain engineer to provide manual input about how to decompose a planning problem. Even HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer to annotate the tasks with information about what to learn. We introduce CURRICULAMA, an HTN method learning algorithm that completely automates the learning process. It uses landmark analysis to compose annotated tasks and leverages curriculum learning to order the learning of methods from simpler to more complex. This eliminates the need for manual input, resolving a core issue with HTN-MAKER. We prove CURRICULAMA’s soundness, and show experimentally that it has a substantially similar convergence rate in learning a complete set of methods to HTN-MAKER. Copyright © 2024 by the authors.

关键词： Learning algorithms

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