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AI-driven Risk Assessment and Decision-making in Healthcare Applications Using Machine Learning: A Case Study on Customer Twitter Feeds 2

AI-driven Risk Assessment and Decision-making in Healthcare ...

引用

2nd International Conference on Computational Intelligence and Network systems, CINS 2024

作者： Darwiesh, Abdelaziz Elhoseny, Mohamed El-Baz, A.H. Atassi, Reem Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. Faculty of Computing and Info University of Sharjah Sharjah United Arab Emirates Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt Higher Colleges of Technology Dubai United Arab Emirates

ISBN: (纸本)9798331504106

This study presents an AI-driven strategy for enhancing decision-making in healthcare applications by assessing and managing potential risks through the analysis of customer interactions on social media platforms, specifically Twitter. We propose the use of machine learning models to outperform traditional lexical approaches, addressing the complexities inherent in risk detection. Natural language processing methods are employed to efficiently process and analyze textual data, enabling healthcare institutions to make informed decisions about risk management. By leveraging customer feedback from the X platform, our approach aids in the development of effective healthcare programs by identifying and classifying risks. We create three specialized datasets for risk analysis, risk assessment, and hazard classification to optimize the training and dep.oyment phases. In our case study, which focuses on tweets related to healthcare for US consumers, we find that over 50% of the sample identifies potential risks, with nearly 80% indicating medium levels of risk. Commonly identified risks include financial, operational, and strategic threats. The proposed methodology demonstrates an average accuracy of 82.66% in risk detection, identification, and assessment, confirming its effectiveness in supporting AI-driven decision-making in healthcare application development. ©2024 IEEE.

关键词： Tweets

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Lexicon approach for risk analysis in emerging technologies

Lexicon approach for risk analysis in emerging technologies

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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Elhoseny, Mohamed Darwiesh, Abdelaziz El-Baz, A.H. University of Sharjah Faculty of Computing and Informatics Information Systems Dep. Sharjah United Arab Emirates Damietta University Faculty of Science Mathematics Dep. New Damietta Egypt Damietta University Faculty of Computers and Artificial Intelligence Computer Science Dep. New Damietta Egypt

ISBN: (纸本)9798350322972

This article proposes a novel methodology to analyze risks in digital currencies. It dep.nds on the perceptions of users in social media to recognize and estimate the potential risks. Besides, it includes utilizing high-level computational techniques such as big data analysis techniques and natural language processing. We illustrate the suggested system mathematically and some important expressions for risk analysis, risk recognition and risk estimation are driven in closed forms. Moreover, we present a use case study to extract insights about the potential risks related to digital currencies. Further, some indicators are provided to illustrate the effectiveness of the suggested approach like the mean accuracy for processes of analyzing risks, identifying risks and assessing risks assessment that is 77 %. © 2023 IEEE.

关键词： Risk management

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Lexicon-enhanced machine learning techniques for predicting risks in cryptocurrencies 4

Lexicon-enhanced machine learning techniques for predicting ...

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Darwiesh, Abdelaziz Elhoseny, Mohamed El-Baz, A.H. Nour, Mohamed Abdallah Mathematics Dep Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt

ISBN: (纸本)9781839539954

This study presents a new approach to predict cryptocurrencies risks based on social media platform indicators. This method can help cryptocurrency investors in their future decision-making. It utilizes advanced techniques such machine learning and natural language processing. Furthermore, a case study on American cryptocurrency investors is provided to help them identify and assess the risks associated with their purchases. Moreover, performance metrics are computed to demonstrate the capabilities of the new approach. © The Institution of Engineering & Technology 2023.

关键词： Adversarial machine learning

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AI-driven Risk Assessment and Decision-making in Healthcare Applications Using Machine Learning: A Case Study on Customer Twitter Feeds

AI-driven Risk Assessment and Decision-making in Healthcare ...

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Computational Intelligence and Network systems (CINS), International Conference on

作者： Abdelaziz Darwiesh Mohamed Elhoseny A.H. El-Baz Reem Atassi Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. Faculty of Computing and Info. University of Sharjah Sharjah UAE Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt Higher Colleges of Technology Dubai UAE

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

关键词： Training Accuracy Social networking (online) Computational modeling Decision making Blogs Medical services Natural language processing Complexity theory Risk management

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Lexicon approach for risk analysis in emerging technologies

Lexicon approach for risk analysis in emerging technologies

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Mohamed Elhoseny Abdelaziz Darwiesh A. H. El-Baz Information Systems Dep. Faculty of Computing and Informatics University of Sharjah Sharjah UAE Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt

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Smart Harvesting based on Image Processing

Smart Harvesting based on Image Processing

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2020 International Computer Symposium, ICS 2020

作者： Joseph, S.P Wijerathna, L.L.M.C. Epa, K.G.R.D. Egalla, E.K.W.A.P.K. Abeygunawardhana, P.W.K. De Silva, R Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Faculty of Computing Malabe10115 Sri Lanka Sri Lanka Institute of Information Technology Dep. of Software Engineering Faculty of Computing Malabe10115 Sri Lanka

ISBN: (纸本)9781728192550

Vision device is a critical component of fruit harvesting mobile robot which is designed for recognizing and harvesting of tomato fruits. The system of vision device of the moving robot, for a dedicated path identification with the use of colour detection algorithms and contour detection algorithms could be stated as an additional usage of the image-based technology. When the robot operates in real life, varying environmental parameters would not affect its activity, since the robot will be functioning mainly indoors, specifically in a greenhouse. Robust fruit segmentation algorithms for the visual system and the fruit plucking mechanism using a readymade robot arm with a soft gripper where it operates with basic hardware components like motors, controlled by a microcontroller with the intervention of kinematic theories, is being used with the goal of collecting fruit objects efficiently in the natural world. Plucking only the ripe and healthy object fruit without damaging both the fruit and the tree is the intended task of the system. The aim is to reduce human effort by promoting automation concepts in agriculture in developing countries like Sri Lanka with great potential, where agriculture plays a vital role in the development of the economy while indoor plantation and harvesting mechanism are also being developing to cater to the economy of the country as well. The way that this system is implemented will be discussed in this paper. © 2020 IEEE.

关键词： Fruits

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Smart Monitoring and Disease Detection for Robotic Harvesting of Tomatoes 3

Smart Monitoring and Disease Detection for Robotic Harvestin...

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3rd International Conference on Advancements in computing, ICAC 2021

作者： Pasindu, Imesh Viraj, Sumeera Dilshan, Ravindu Kalhara, Akshith Senaweera, Oshada De Silva, Rajitha Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe Sri Lanka Lincoln Institute for Agri-Food Technology University of Lincoln Lincoln Agri-Robotics Centre United Kingdom Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665408622

Tomato is a one of the most popular produced and extensively consumed vegetables in the world. Typical agricultural systems make extensive use of human labor which is more costly and less effective. This research explores the minimization of human labor through automation. The diseases infected by tomato plants are hard to detect. Identifying these diseases in advance would save the cultivation of the disease from spreading, thereby saving the crop. It is also a difficult task to recognize the ripe harvest and experienced labor is required. The efficiency of the harvesting method will be increased by automating the identification process of ripened fruits. Manually picking tomatoes can cause some harm to the fruits during plucking due to inconsistencies in human labor. Such damage will be reduced through a better implemented robotic scheme. This paper presents the development of autonomous system for tomato harvesting and disease detection. © 2021 IEEE.

关键词： Harvesting

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Framework for modelling and implementing secure NoSQL document databases 22

Framework for modelling and implementing secure NoSQL docume...

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22nd Jornadas de Ingenier"a del Software y Bases de Datos, JISBD 2017 - 22nd Conference on Software Engineering and Databases, JISBD 2017

作者： Blanco, Carlos Peral, Jesus Trujillo, Juan Fernández-Medina, Eduardo GSyA research group Institute of Information Technologies and Systems. Information Systems and Technologies Department University of Castilla-La Mancha Spain ISTR research group Dep. of Computer Science and Electronics University of Cantabria Spain LUCENTIA research group Languages and Computing Systems Department University of Alicante Spain

The great amount of data managed by Big Data technologies have to be correctly assured in order to protect critical enterprise and personal information. Nevertheless, current security solutions for Big Data technologies such as NoSQL databases do not take into account the special characteristics of these technologies. In this paper, we focus on assuring NoSQL document databases proposing a framework composed of three stages: (1) the source data set is analysed by using Natural Language Processing techniques and ontological resources in order to detect sensitive data. (2) we define a metamodel for document NoSQL databases that allows designer to specify both structural and security aspects. (3) this model is implemented into a specific document database tool, MongoDB. Finally, we apply the framework proposed to a case study with a data set of medical domain. © 2017 Sistedes. All rights reserved.

关键词： Database systems

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Scrum to Manage Humans and Intelligent systems

Scrum to Manage Humans and Intelligent Systems

引用

International Conference on computing for Sustainable Global Development

作者： Georgios Tsaramirsis Abdullah Basahel Dep. Of Information Technology Faculty of Computing and IT King Abdulaziz University Dep. Of Management Information Systems Faculty of economics and management King Abdulaziz University

ISBN: (纸本)9781467394178

This paper study the applicability of the SCRUM agile project management methodology to teams that contain humans and other intelligent entities such as robots, non-playable characters in video games and other autonomous intelligent systems. The paper includes an example that shows how humans and autonomous intelligent systems can work together in a team, as well as discussion for how the intelligent units can participate in the rest of Scrum activities. This paper only examines teams that include both autonomous intelligent systems and humans only. Teams that contain only robots or only humans are out of the scope of this research.

关键词： Project management Scrum agile project management Robot-human collaboration

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Spatial-spectral hyperspectral image clustering using cluster's bands box-plots

Spatial-spectral hyperspectral image clustering using cluste...

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European Conference on Data Mining 2014 and International Conferences on Intelligent systems and Agents 2014 and Theory and Practice in Modern computing 2014

作者： Al Moghalis, Mohamed A. Hegazy, Osman M. Imam, Ibrahim F. El-Bastawessy, Ali H. Dep. of Information Systems College of Computer and Information Systems Alyamamah University KSA Saudi Arabia Dep. of Information Systems Faculty of Computers and Information Cairo University Egypt Dep. of Computer Science College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Egypt Dea. of Faculty of Computer Science MSA University Egypt

ISBN: (纸本)9789898704108

The attention given recently for Hyperspectral Images (HSI) in remote sensing was due to its spectral nature used in earth surface exploration. Recently, researches proposed approaches that combine features of spatial nature with spectral nature to enhance HSI analysis accuracy. The reason behind this orientation is that contiguous pixels mostly share spectral features due to the low spatial resolution. In this paper, authors follow this orientation in clustering HSI using K-means. The proposed approach uses kernels, group of spatially neighbored pixels, to build profile for each cluster. The profile preserves cluster's spectral nature through its bands' Box-Plots that were extracted from selected kernels. The approach starts by selecting K kernels from the given image scene randomly instead of generating random kernels. Then a profile for each cluster is built using the selected kernel pixels' spectrums. The profile consists of b box-plots, where b is the number of bands. Each band box-plot interprets the spread of contiguous pixels' reflectance values in that band. Each pixel in the image will join the nearest cluster. The distance to be measured between any given pixel spectrum and cluster's centroid is replaced by counting how many reflectance values have been considered outlier to their corresponding band's box-plot in that cluster profile. The pixel will join the cluster with minimum outlier count. The profiles are updated iteratively using the new pixels distribution. Data sets used in the experiments are captured by Hyperion Earth Observer 1 (EO-1) sensor. Copyright © 2014 IADIS Press All rights reserved.

关键词： Pixels

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