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Detecting COVID-19 spreading phases with time series analysis

Detecting COVID-19 spreading phases with time series analysi...

引用

2022 International Conference on computer Application and Information Security, ICCAIS 2022

作者： Kuang, Yuanguo Zheng, Jingyao Song, Haiyan Dept. of Engineering University of Cambridge Cambridge United Kingdom School of Computer and Data Engineering NingboTech University Zhejiang Ningbo315100 China

ISBN: (纸本)9781510663459

Piecewise linear regression (PLR) method is applied to study cumulative cases of COVID-19 evolving everyday in England up to 6th February 2022 just before travel restrictions are removed and people started not to get tested anymore in the UK and factors e.g. the lockdowns behind the spread COVID-19 are also investigated. It is clear that different periods exhibit distinct patterns depending on variants and government-imposed restriction. Therefore, the effectiveness of lockdown measures is evaluated by comparing the rate of increase after a certain period (delay effect of measures) and that of time before as well as how new variants take over as a dominant variant. In addition, autoregression function is studied to show strong effect of cases in the past on today's cases since the disease is highly infectious. Most of work is carried out thorough python built-in libraries such as pandas for preprocessing data and matplotlib which allows us to gain more insight and better visualization into the real scenario. Visualization is conducted by Geoda showing the regional level of infections. © 2023 SPIE.

关键词： COVID-19

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Predictive Maintenance Method using Machine Learning for IoT Connected Computed Tomography Scan Machine 2

Predictive Maintenance Method using Machine Learning for IoT...

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2nd IEEE National Biomedical engineering Conference, NBEC 2023

作者： Shah Ershad Bin Mohd Azrul Shazril, Mohammad Habib Mashohor, Syamsiah Amran, Mohd Effendi Fatinah Hafiz, Nur Rahman, Aishah A Ali, Azizi Rasid, M. Fadlee A. Safwan A. Kamil, A. Azilah, Nurul Farhana Universiti Putra Malaysia Systems Engineering Faculty of Engineering Dept. of Computer and Communication Selangor Serdang43400 Malaysia Biomedical Sector Engineering Service Division Ministry of Health Putrajaya62590 Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350338546

Predictive maintenance on medical equipment is to identify the equipment condition and to forecast when the maintenance is required. In this paper, the Computed Tomography scan machine is deployed with IoT sensors to monitor the environment and machine parameters such as temperature and humidity of the room, current, radiation and acceleration of X axis and Y axis. The readings are processed to ensure they are according to normal distribution and the current readings are acquired when then machine is working well. To generate data of faulty machine condition, synthetic data is created. Data synthesis is also needed to generate more data for the purpose of model's training for machine learning. An artificial neural network has been designed to predict the breakdown probability of the machine and the inputs are all the parameters from the IoT sensors. The collected readings are acquired since September 2022 until January 2023 from one of the hospitals in Klang Valley. The model produced a very good prediction accuracy of 95.91 percent which makes it an acceptable model for the prediction of machine breakdown. The prediction output is also displayed with real data monitoring on the dashboard, hosted by a dedicated server. © 2023 IEEE.

关键词： computerized tomography

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SCN-DRL: Scheduler for large-scale Critical Notification applications based on Deep Reinforcement Learning 9

SCN-DRL: Scheduler for large-scale Critical Notification app...

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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Vrbaski, Mira Bolic, Miodrag Majumdar, Shikharesh University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada Carleton University Dept of Systems and Computer Engineering Ottawa Canada

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

A multi-objective Scheduling approach for large-scale microservice Critical Notification system applications (SCN-DRL) based on Deep Reinforcement Learning is presented. This paper addresses optimization for three objectives: cloud service cost, cloud resource utilization, and system notification deadline. Recent advances in the deep reinforcement learning field inspired us to research how to build a solution that learns to manage the cloud container's resources directly from experience. SCN-DRL is an example solution that translates the containerized microservice cloud cluster scheduling problem into a learning problem. To the best of our knowledge, this is the first work dealing with multi-optimization objectives for critical microservice notification applications using deep reinforcement learning, where the notification load is variable and depends on the results of the other previously prior processed microservice. Our initial results show that SCNDRL performs better than state-of-The-Art heuristics and adapts to different workload conditions without a noticeable drop in performance. © 2022 IEEE.

关键词： Containers

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A Novel Interoperability Evaluation Framework for the Warehouse Management System 49

A Novel Interoperability Evaluation Framework for the Wareho...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Thayil, Tijo Zhang, Jingbing Vadakkepat, Prahlad Al Mamun, Abdullah Sagar, Krishna Singapore637143 Singapore College of Design and Engineering National University of Singapore Dept. of Electrical and Computer Engineering Singapore Singapore637143 Singapore

ISBN: (纸本)9798350331820

Warehouse management is a critical element in the supply chain. The rise of e-commerce demands 'any-mix any-volume' customized and personalized orders. Due to the variety of products in 'any-mix any-volume' scenario the warehouse operations have become complex. The advent of multi-channel commerce has reduced the minimum order quantity (MOQ), claiming frequent changes. The 'any-mix any-volume' operations require flexible automation for product storage, retrieval, packing and shipping. Seamless operations of multiple devices are essential for automating flexible warehouses. For effective flexible operations, the Warehouse Management System (WMS) requires controlling the automation devices and associated auxiliary systems. Heterogenous automation devices with distinct communication protocols, data formats and connectivity are challenges for WMS centered control. An interoperable WMS can enable seamless communication and data exchange among devices. In this work, the interoperability of the WMS and various aspects of interoperability of engineering systems are presented. Different WMS system designs are considered and evaluated for the interoperability requirements. A framework for evaluating the interoperability of a WMS is proposed which is validated through experimental trials at an industry grade warehouse. © 2023 IEEE.

关键词： Interoperability

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Virtual Reality Based Medical Training Simulator and Robotic Operation System

Virtual Reality Based Medical Training Simulator and Robotic...

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2022 International Conference on Recent Progresses in Science, engineering and Technology, ICRPSET 2022

作者： Rahman, Md. Mustafizur Ishmam, Md Fatin Hossain, Md. Tanvir Haque, Md. Emdadul Dept. of Information and Communication Engineering Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350332704

In the medical sector learning about the human body and practicing surgeries, the medical students need a real human body which is very costly. Repeatedly practice or undone after practice is almost impossible with the human body. Human anatomy, physiology, etc. subjects are very hard to understand and learn from books and images. Learning surgery on living humans is sometimes unethical and trainee surgeons has to struggle to learn rare surgeries. To solve these problems we use virtual reality (VR) to build a medical training simulator that can provide medical students better understanding and learning about the human body through 3D models of the body. Students can get an interactive learning experience and see how organs work from the molecular level and perform several operations. Since this is a simulation everything a new learner will do can be undone and re-practiced. On the other hand skilled surgeons are not available in all places in the country especially in remote places. A patient who needs the critical surgical treatment has to travel to a city to take the treatment by the expert surgeons. To solve this problem in this project we also connect a robot to our VR based system. A skilled surgeon can operate in the virtual environment from a distant place using the robot. The vision system of the robot can be realized by the surgeon through VR headset and he or she feels like, he or she is present there. Surgeons can control the robotic hands and tools necessary to perform the surgery from the remote place. This will reduce the trouble of patients traveling from remote places to city area for surgery. We implement the demo system in the lab and show some results which indicate the applicability of the proposed system. © 2022 IEEE.

关键词： Robotic surgery

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Interpretable Deep Learning for Skin Cancer Detection: Exploring LIME and SHAP 15

Interpretable Deep Learning for Skin Cancer Detection: Explo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sah, Nabin Kumar Reddy, M. Vivek Srikar Ullas, Karthik Singh, Tripty Mandal, Adhirath Chatterji, Suman Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Uttarakhand Dehradun India Dept of Mechanical Engineering Kongju National University Cheonan Korea Republic of

ISBN: (纸本)9798350370249

An effective treatment for skin cancer necessitates early detection, a global problem. This work introduces a unique deep learning technique that extracts important characteristics from skin lesion photos by using CNNs,Resnet50, VGGNet, and DenseNet. The integration of LIME and SHAP approaches improves interpretability by emphasising significant portions of the picture. According to the model's competitive performance criteria (specificity, sensitivity, and accuracy), dermatologists may be able to depend on it to assist in the early identification of skin cancer. The research also examines the possible advantages and drawbacks of deep learning for the diagnosis of skin cancer, emphasising the technology's potential to advance healthcare by strengthening diagnostic abilities. ©2024 IEEE.

关键词： Lung cancer

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Web-based Platform - a Defining Factor for Career Orientation Under the Conditions of a Pandemic 8

Web-based Platform - a Defining Factor for Career Orientatio...

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8th International Conference on Energy Efficiency and Agricultural engineering, EE and AE 2022

作者： Evstatiev, Ivan Georgiev, Georgi University of Ruse Dept. of Electronics Ruse Bulgaria University of Ruse Dept. of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9781665407090

The flow of information exchange between employers and job seekers - students, PhD students, alumni - are analyzed. The tasks of the career centres to organize and direct the information flows for vacancies in the conditions of a pandemic are presented. A web-based platform is presented, which supports a database for students, PhD students and alumni. The platform allows employers to prepare and send messages to students, taking their professional fields and specialties into account. The relational schema of the database is also presented, as well as the process of publishing a message in the system. © 2022 IEEE.

关键词： Students

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Predict of the Hidden Drug Interactions Based on Similarity Utilizing the Knowledge Graph

Predict of the Hidden Drug Interactions Based on Similarity ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Elfiky, Mariam Gamal Ghoniemy, Samy Eldin, Ahmed Sharaf University of Hertfordshire hosted by Gaf Dept. of Information Technology Management for Business Cairo Egypt Galala University Faculty of Computer Science and Engineering Cairo Egypt Sinai University Faculty of Information Technology and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Drug-drug interactions (DDIs) can significantly impact treatment outcomes by causing adverse effects or reducing therapeutic efficacy. Whereas many DDIs are already known, there are numerous hidden interactions. Therefore, this study tries to predict unknown interactions based on drug structure and target information from the Drug Bank dataset. This paper trains the random forest model integrated with a knowledge graph on a dataset containing 922 unique drugs and 731,196 drug interactions. To illustrate the complex interactions between these drugs, we created a knowledge graph with Neo4j. This graph integrates information gathered from the dataset and extracts relevant data using Cypher queries. The inference engine was created according to the model's predictions on unknown DDIs. The random forest model, with an F1-score of 0.82, achieved a recall of 0.99 and a precision of 0.70. Also, 34,580 potential DDIs were extracted from the sample dataset of drugs. By embedding the knowledge network, our inference engine significantly enhanced predicting the hidden interactions and was found strong in the balance of precision and recall. Our research is applicable to the enhancement of drug safety and effectiveness via mitigating potential DDIs by identifying them. © 2024 IEEE.

关键词： Knowledge graph

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Automated Testing of Standard Conformance for Robots 19

Automated Testing of Standard Conformance for Robots

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

作者： Sohail, Salman Omar Schneider, Sven Hochgeschwender, Nico Institute of Ai and Autonomous Systems Dept. of Computer Science Hochschule Bonn-Rhein-Sieg Sankt Augustin Germany Mybotshop GmbH Germany

ISBN: (纸本)9798350320695

Verifying that robots conform to standards is an essential part of any responsible development process. A common technique to verify whether robots faithfully meet the requirements of standards is testing. Unfortunately, conformance testing is, to a large extent, a manual and therefore costly exercise which needs to be repeated for every robot under test. In this paper, we propose an automated approach to robot standard conformance testing (RSCT). Based on an analysis of conformance clauses in robot standards (e.g., ISO 23482-1 and ISO 10218) we identify, harmonise and formalise high-level properties representing the assertions to be tested. To verify whether properties hold, we put forward a Robot Test Definition Language (RTDL) which enables developers to specify reusable test scenarios which can be executed in simulation. We evaluate the approach with four heterogeneous robot platforms, identifying previously undiscovered defects in a robot driver. Our approach enables the automated testing of 40% of the conformance clauses defined in ISO 23482-1. © 2023 IEEE.

关键词： Robots

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