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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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Impact of Machine Learning in Digital Marketing Applications 3

Impact of Machine Learning in Digital Marketing Applications

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3rd International Informatics and Software engineering Conference, IISEC 2022

作者： Gursoy, Gunes Varol, Asaf Varol, Serkan Maltepe University College of Engineering and Natural Sciences Dept. of Computer Engineering İstanbul Turkey The University of Chattanooga College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

The devastating Covid 19 pandemic has shifted priorities in the business world to accommodate the new normal that the pandemic has caused. Digital marketing has become a necessity for corporations to keep up with the evolving needs of consumers. With the help of the emerging technologies, corporations have begun embracing tailored digital marketing applications that aim to attract and retain more consumers. The techniques or applications used by businesses to improve their marketing performance are consolidated and supported by artificial intelligence applications. In this study, we examine the significance of the marketing field and machine learning, as well as marketing techniques, artificial intelligence, and machine learning algorithms. As a result of the examinations, it has been shown how machine learning techniques and algorithms facilitate marketing processes, increase the profitability of businesses and it is very difficult to realize digital marketing without machine learning algorithms. © 2022 IEEE.

关键词： Marketing

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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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Leveraging UAV Technology for Enhanced Disaster Management: Innovations and Applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Upadhyay, Govind Murari Vats, Prashant Shanker, Surabhi Bhushan, Saloni Mittal, Sonu Mandot, Manju Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India K.R. Mangalam University School of Engineering & Technology Dept. of CSE Sohna Road Haryana Gurugram India Chhatrapati Shivaji Maharaj University Dept. of Computer Science/IT Maharashtra Panvel India Amity Institute of Information Technology Amity University Noida Dept. of CSE Uttar Pradesh India J. R. N. Rajasthan Vidyapeeth University Dept. of Computer Science/IT Rajasthan Udaipur India

ISBN: (纸本)9798350385199

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field. © 2024 IEEE.

关键词： Disaster prevention

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Detection of Multi-Class Multi-Label Ophthalmological Diseases in Retinal Fundus Images Using Machine Learning

Detection of Multi-Class Multi-Label Ophthalmological Diseas...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Karule, P.T. Bhele, Sujata B. Palsodkar, Prasanna Agarkar, Poonam T. Hajare, Hirendra R. Patil, Prashant R. Yeshwantrao Chavan College of Engineering Dept. of Electronics Engineering M.S Nagpur India Government College of Engineering Dept. of Electronics & Telecommunication Engineering M.S Nagpur India Ballarpur Institute of Technology Dept. of Computer Science and Engineering M.S Chandrapur India Smt. Radhikatai Pandav College of Engineering Dept. of Management Studies M. S Nagpur India

ISBN: (纸本)9798350319019

Multi-disease diagnosis of fundus image is a great challenge even though sophisticated computer-aided tools are available and ophthalmologist experience is to par. Clinical tests capture abnormalities and anatomical structures for various diagnoses of the retinal image. The proposed work uses RFMiD (Retinal Fundus Multi-Disease Image Dataset) to classify ocular diseases using an efficient feature extraction framework and machine learning. The framework considers 8-12 diverse ophthalmological diseases from 3200 dataset images labeled with the help of two senior medical (retinal) experts. Experimental evaluation of the dataset using Random forest classified up to 10 diseases with an accuracy of 100% while 96.75% was obtained when 12 diseases were considered without augmenting the dataset images. The evaluation successfully determined the retinal infection due to fine-grained variability, appearance, and closeness in the presence of several lesions. © 2024 IEEE.

关键词： Classification (of information)

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A/B Testing and Audience Creation for Effective Digital Marketing: Evidences from Facebook Analytics 6

A/B Testing and Audience Creation for Effective Digital Mark...

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6th International Conference on Information systems and computer Networks, ISCON 2023

作者： Singh, Nripendra Jaiswal, Anand Singh, Prakhar Sharma, Dilip Kr. PennWest University Dept. of Management and Marketing Clarion United States New Delhi Institute of Management Dept. of Operations and Scm Delhi India PennWest University Computer Information Science Department Clarion United States GLA University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350346961

Digital marketers use a number of strategies, including custom audiences, lookalike audiences, ad creative, and A/B testing, to organize and enhance how they present advertisements to consumers. The purpose of this research is to show an effective method and process for running advertisements on Facebook more efficiently. The study has taken a case study of a company- Great Deal Tires and analyzed the Facebook analytics data and insights with A/B testing and audience creation approach to assess the digital marketing impact. The study has implemented three test strategies for designing and placing Facebook ads for Great Deal Tires in order to assess the marketing impact. In the first test, a custom audience was chosen, and an identical Facebook video ad's impact was evaluated based on the duration of the ad. In the second test, A/B testing was used to test Facebook ads, and the results of the tests were compared across three different audience groups to determine how well the ads performed. The third test evaluated on the audience conversion in the terms of subscribing to the Page, in which three different ads were shown to three distinct audience groups in order to compare the effects of the various ads on the diverse audiences. The study's findings provided important information about Facebook ad placement, its effects on various audience segments, and digital marketing strategies to increase audience outreach. The study is important in demonstrating that Facebook advertisements are an effective marketing medium and a digital marketer can achieve good outreach with a modest investment of funds.. © 2023 IEEE.

关键词： Marketing

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Social Robot Navigation with Adaptive Proxemics Based on Emotions

arXiv

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arXiv 2024年

作者： Bilen, Baris Kivrak, Hasan Uluer, Pinar Kose, Hatice Dept. of AI and Data Engineering Istanbul Technical University Turkey Dept. of Computer Engineering Karabuk University Turkey Dept. of Computer Engineering Galatasaray University Turkey

The primary aim of this paper is to investigate the integration of emotions into the social navigation framework to analyse its effect on both navigation and human physiological safety and comfort. The proposed framework uses leg detection to find the whereabouts of people and computes adaptive proxemic zones based on their emotional state. We designed several case studies in a simulated environment and examined 3 different emotions;positive (happy), neutral and negative (angry). A survey study was conducted with 70 participants to explore their impressions about the navigation of the robot and compare the human safety and comfort measurements results. Both survey and simulation results showed that integrating emotions into proxemic zones has a significant effect on the physical safety of a human. The results revealed that when a person is angry, the robot is expected to navigate further than the standard distance to support his/her physiological comfort and safety. The results also showed that reducing the navigation distance is not preferred when a person is happy. © 2024, CC BY.

关键词： Navigation

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Kubernetes Cluster Disaster Recovery Using AWS 7

Kubernetes Cluster Disaster Recovery Using AWS

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhavsar, Simran Agrawal, Apoorva Ropalkar, Tanvi Kamdi, Pranjali Hajare, Aparna Deshpande, Shilpa Rathi, Rahul Garg, Devendra Cummins College of Engineering for Women Dept. of Computer Engineering Pune India Veritas Software Technologies India Private Limited Pune India

ISBN: (纸本)9798350304268

As applications in IT have been spanning multiple containers across servers, kubernetes is being used extensively for automating software deployment, scaling and management. Kubernetes eases the container tasks and also other activities like horizontal scaling and canary development. To run cloud-native applications, kubernetes has become the de facto standard for container orchestration, regardless of the underlying platform. This makes it very crucial to have a disaster recovery plan for kubernetes environments. This paper proposes a system for backup and restore of a kubernetes cluster using the snapshot method along with the Public Key Infrastructure (PKI) certificates of a cluster. It also focuses on automating the backup and restore operations using microservices. Furthermore, it makes use of Amazon Web Services (AWS) tools for the reliability, scalability and the automation of backup and restore operations. The user can run the proposed microservices to backup and restore the cluster from any specific timestamp before the disaster. The experimental results demonstrate the effective performance of the proposed method in terms of efficiently restoring the kubernetes cluster in optimal time. © 2023 IEEE.

关键词： Cluster backup Cluster initialization Cluster setup Disaster recovery Kubernetes Master node Restore Worker node

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Detecting COVID-19 spreading phases with time series analysis

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

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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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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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