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Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention 11

Predicting Outcomes in Long COVID Patients with Spatiotempor...

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11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Hao, Degan Negahdar, Mohammadreza University of Pittsburgh Intelligent Systems Program Pittsburgh United States Research and Early Development Genentech Inc. San Francisco United States

ISBN: (纸本)9798350302639

Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-term complications in COVID-19 could benefit the treatment planning and resource arrangement. However, due to the heterogeneous phenotype presented in long COVID patients, it is difficult to predict their outcomes from their longitudinal data. In this study, we proposed a spatiotemporal attention mechanism to weigh feature importance jointly from the temporal dimension and feature space. Considering that medical examinations can have interchangeable orders in adjacent time points, we restricted the learning of short-term dependency with a Local-LSTM and the learning of long-term dependency with the joint spatiotemporal attention. We also compared the proposed method with several state-of-the-art methods and a method in clinical practice. the methods are evaluated on a hard-to-acquire clinical dataset of patients with long COVID. Experimental results show the Local-LSTM with joint spatiotemporal attention outperformed related methods in outcome prediction. the proposed method provides a clinical tool for the severity assessment of long COVID. © 2023 IEEE.

关键词： COVID-19

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A Surrogate Urban Building Energy Model for Predicting Cooling Energy Consumption in a Hot and Arid Climate 9th

A Surrogate Urban Building Energy Model for Predicting Cooli...

引用

9th international Building Physics conference

作者： Ahmed, Omar Wang, Liangzhu (Leon) Hassan, Ibrahim Galal Concordia Univ Dept Bldg Civil & Environm Engn Ctr Zero Energy Bldg Studies Montreal PQ Canada Texas A&M Univ Qatar Mech Engn Program Doha Qatar

ISBN: (纸本)9789819783113;9789819783090;9789819783083

Building Performance Simulation (BPS) is a potent and commonly utilized method to assess and improve building design, operation, and overall energy performance. Commonly used physics-based BPS tools often entail a high computational cost and require a significant amount of technical data and multiple assumptions for energy predictions. While data-driven approaches are contingent upon the availability of large enough volumes of high-quality data for training the prediction models. Surrogate models, which combine physics-based simulations with components of statistical or machine learning techniques have demonstrated their ability to harness the advantages of both physics-based and data-driven models to achieve superior prediction results. In this study, a surrogate model is utilized to predict the cooling energy consumption for a mixed-use district. A hybrid approach is employed, which combines machine learning techniques with representative building physics-based archetypes. High-rise building archetypes, representative of the building stock in the district, are modeled using EnergyPlus software. A parametric simulation is then automated using RStudio by modifying crucial parameters related to the building envelope characteristics, building geometry, and building operational parameters, including occupant behavior. the proposed methodology offers a solution for a more accurate representation of occupant behavior in machine learning surrogate building energy models. the artificial dataset generated is then used to train three neural network models, one for each of the predominant building typologies available in the district. the predictions made by the machine learning models are then benchmarked against unseen EnergyPlus results, and the prediction performance of the models is validated using findings in the literature.

关键词： Surrogate Modeling Machine learning Building Performance Analysis

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Potential Technological Advancements in the Future of Process Control and Automation 11

Potential Technological Advancements in the Future of Proces...

引用

11th IEEE Open conference of Electrical, Electronic and Information Sciences, eStream 2024

作者： Kalyan Chakravarthi, M. Pavan Kumar, Y.V. Pradeep Reddy, G. School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Kookmin University Sungbuk-Gu Seoul02703 Korea Republic of

ISBN: (纸本)9798350352412

the potential technical breakthroughs in the field of process control and automation are a subject of significant interest and research. these advancements have the potential to transform several industries by enhancing safety, productivity, and sustainability in manufacturing processes. One potential advancement is the integration of emerging technologies such as artificial intelligence (AI) and machine learning (ML) to process control systems. AI and ML are anticipated as key catalysts in improving decision-making abilities and optimizing processes. Besides, they can analyze large amounts of data in real-Time industrial processes and can perform the necessary predictions. Further, the integration of IoT modernizes and creates a seamless network of interconnected devices with real-Time communication and monitoring facilities. It opens a path for remote control, enhancing safety measures, and reducing operational costs. Moreover, cybersecurity becomes progressively vital as systems become more interconnected. Robust measures to safeguard against potential cyber threats will be imperative to ensure the integrity/reliability of automated processes. With this motivation, this paper discusses various futuristic and revolutionary aspects of process control and automation. these technical advances not only increase efficiency/precision but also herald a new era of intelligent systems that can handle dynamic industrial backdrops. © 2024 IEEE.

关键词： Process control

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Model of Text-to-Text Transfer Transformer for Hadith Question Answering System 11

Model of Text-to-Text Transfer Transformer for Hadith Questi...

引用

11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Sulistio, Bambang Suhartono, Derwin Soeparno, Haryono Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Program Jakarta11480 Indonesia

ISBN: (纸本)9798350389289

the advancement of Artificial Intelligence (AI), notably in Natural Language Processing (NLP), has been remarkable. Among its applications, Question-Answering (QA) systems stand out, assisting users in accessing pertinent information across various topics, including religious contexts. Religion plays a significant role in providing guidance on moral values to modern society, such as tolerance, compassion and social norms. Religious practices taught by religion serve as significant foundations for addressing the increasingly advancing influence of technology. the primary goal of this paper is to create a question-answering system proficient in analyzing user inquiries and precisely extracting data from translated Indonesian Hadith datasets. this research endeavors to enhance question-answering accuracy through Deep learning techniques. Employing pre-processing methods, the system interprets user query intents. Furthermore, the Text-to-Text Transfer Transformer (T5), a text-based language Transformer model, is utilized to streamline the retrieval process for Hadith-related queries based on relevant subjects. the research findings demonstrate the precision of answers through BLEU and ROUGE scores. the novelty of this study lies in the creation of a dataset of hadith translations in Indonesian and the fine-tuning of the T5 model that has been trained using a dataset of questions and answers based on these translations. this research demonstrates the following evaluation scores: BLEU: 0.606, ROUGE-1: 0.702, ROUGE-2: 0.548, and ROUGE-L: 0.701, indicating that the answers generated by this particular research model are better compared to those produced using the standard mT5 model. © 2024 IEEE.

关键词： Deep learning

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intelligent Online Shopping using ML-based Product Comparison Engine 6

Intelligent Online Shopping using ML-based Product Compariso...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Harikirshnan, K. Nagavigneshwar, R. Vignesh, R. Santhosh, R. Reshma, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

A relatively recent technique for gathering data online is known as scraping. the automated process involves the exploration of e-commerce websites and obtaining certain data, such as pricing, reviews, quality features, etc. Sentiment analysis of product attributes on e-commerce platforms, such as pricing, reviews, quality, etc., can substantially increase user satisfaction in the e-commerce industry. It continues to be difficult to envision precise sentiment analysis. this research work summarizes the product comparison website that implements intelligent webscraping. the website has a processing model that uses the Machine learning (ML)-based product comparison engine. the simulation analysis for model training and testing uses the Github dataset and the S upport Vector Machine (S VM) algorithm. the S VM classifies the product from various websites based on the rank value, assigned based on the selected features of the product. the proposed model is compared with other ML algorithms such as Decision Tree, Naïve Bayes, and Random Forest. the comparative analysis shows that the S VM algorithm can classify the best products with the highest accuracy ratio of 94.71% over other algorithms. © 2023 IEEE.

关键词： Engines

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YOLOv8-Powered Multi-Object Detection for Safety Helmets and Number Plates in Real-Time Surveillance Systems 5

YOLOv8-Powered Multi-Object Detection for Safety Helmets and...

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5th IEEE international conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Pramila, S. Anil, D. Sirisha, O. New Horizon College of Engineering Bengaluru India Uvce Bangalore University Department of Cse Bengaluru India G.Pulla Reddy Engineering College Department of Computer Science and Business Systems Kurnool India

ISBN: (纸本)9798350379884

this paper presents a real-time object detection system using the latest YOLOv8 model to enhance traffic monitoring and safety enforcement. the model was trained to detect helmets, riders and number plates in diverse traffic scenarios, achieving an overall accuracy of 93.4%. Significant improvements were observed in precision, recall and mean Average Precision (mAP) scores due to the combination of an optimized architecture, high-quality data and fine-tuned hyperparameters. the system maintains real-time inference capabilities with a processing speed of 11 ms per image, making it suitable for continuous surveillance and enforcement applications. this study demonstrates the potential of using advanced deep learning techniques for effective and automated traffic rule enforcement. Future work may involve further optimizations and integration with large-scale cloud-based systems for more comprehensive deployments. © 2024 IEEE.

关键词： Deep learning

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Fog and Edge Computing Issues and Challenges - A Review 11

Fog and Edge Computing Issues and Challenges - A Review

引用

11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Anand, Arnaav Agarwal, Pratyush Bajpai, Mann Kashyap, Ankita Gupta, Punit Manipal University Jaipur Department of Computer and Communication Engineering Jaipur India Manipal University Jaipur Department of Computer Science Engineering Jaipur India School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9798350318159

Fog computing is the latest trend in the current scenario where sensors play a key role in managing and analyzing any device. With this huge spread of sensors and cloud comes various issues like security, network delay, resource utilization, and many more. In this work, we have tried to highlight these issues and existing proposed solutions to address these issues. In this architecture cloud is the main resource provider to compute the data so to address this issue resource allocation plays a significant role. Various nature-inspired algorithms and metaheuristic optimization algorithms are a few solutions to improve the performance of FOG and Cloud. © 2023 IEEE.

关键词： Fog computing

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A Review of Sensors and Machine learning in Animal Farming 11

A Review of Sensors and Machine Learning in Animal Farming

引用

11th IEEE Annual international conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2021

作者： Yaseer, Ahmed Chen, Heping Ingram School of Engineering Texas State University Texas78666 United States

ISBN: (纸本)9781665425278

Livestock is the primary source of meat, dairy, eggs, leather, wool, etc. Because of increased demand, increasing herd size maintained by fewer manpower is becoming important. Precision Livestock Farming (PLF) utilizing digital technologies such as smart sensors, advanced controls, intelligent robotics, Internet of things (IoT), big data and machine learning has been emerged to make the animal farming process automated and economically and environmentally sustainable. Animal Farming now requires intelligent techniques to monitor animal welfare and predict animal health 24/7 in real-time. Big data gathered by the IoT devices and sensors are processed with machine learning for prediction and control. this paper reviews the latest sensor technologies and machine learning techniques that can be used as a decision support tool for making the animal farming process more profitable and insightful. © 2021 IEEE.

关键词： Animals

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A Simple and Efficient Deep learning Architecture for Corn Yield Prediction 11

A Simple and Efficient Deep Learning Architecture for Corn Y...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Terliksiz, Anil Suat Altilar, Deniz Turgay İstanbul Technical University Computer Engineering Department İstanbul Turkey

ISBN: (纸本)9798350303513

Agricultural yield estimation plays a very important role in providing the necessary planning and organization to feed the growing world population. Although past to present regression-based, simulation-based and hybrid methods are proposed for agricultural yield estimation, Machine learning (ML) methods have been used in recent years. Among the ML techniques, Deep learning (DL) is one of the most prominent method recently. In DL techniques, either satellite data on which data reduction methods are applied, as well as weather, soil, etc. data or directly processed large satellite images are used. the aim of this study is to propose an efficient and fast DL architecture for corn yield estimation. In the study, only histogram-based satellite data and yield information were used. the proposed architecture combines the Convolutional Neural Network (CNN)'s ability to learn depth dependent features and detect different feature sets using different kernel sizes in parallel. Proposed architecture is experimented for corn yield estimation of 13 states in the U.S. Corn Belt at county-level. Corn yield from 2018 to 2021 are estimated using only Moderate Resolution Imaging Spectroradiometer's (MODIS) last ten years Surface Reflectance (SR) and Land Surface Temperature (LST) data and last ten years yield data obtained from United States Department of Agriculture (USDA) Quick Stats. the preliminary results show that the proposed architecture uses less data and works faster, giving results close to or even better than the studies in the literature. © 2023 IEEE.

关键词： Convolutional neural networks

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Autonomous Unmanned Aerial Vehicle Indoor Navigation Based on Deep learning Approaches 11

Autonomous Unmanned Aerial Vehicle Indoor Navigation Based o...

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11th RSI international conference on Robotics and Mechatronics, ICRoM 2023

作者： Ghasemi, Hamed Balazadeh, Elnaz Malekpour, Shayan Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran University of Tehran Human and Robot Interaction Laboratory Tehran Iran University of Tehran School of Electrical and Computer Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

this article presents two approaches based on deep learning for achieving autonomous navigation and subsequently, conducts an in-depth comparative analysis to elaborate their respective performance. Both methods leverage neural networks to facilitate navigation. the first approach employs a deep convolutional neural network to identify suitable areas for forward movement, while the second method utilizes the drone's front camera to capture images, which are then processed by a generative network to generate corresponding depth images. Importantly, both approaches rely solely on an RGB camera, resulting in cost savings for the robot and reduced energy consumption. the training data used for classifying collision-free and appropriate paths were gathered from simulated environments closely resembling real indoor settings. the proposed model calculates the probability associated with each segment of the input image, enabling the selection of the most viable area for drone flight. Incorporating generative networks as a depth estimation module enhances flight accuracy, making navigation in uncharted environments more straightforward. the accuracy of depth estimation was evaluated using standard criteria, achieving an accuracy rate of 84%. this result underscores the effectiveness of the proposed method. © 2023 IEEE.

关键词： Drones

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