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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

computer Systems science & engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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Use of digital platforms for enhancing academic performance. Systematic Review 7

Use of digital platforms for enhancing academic performance....

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Costescu, Cristina Anamaria Şogor, Mǎlina Pǎlimariu, Sabina Katona, Jozsef Tufar, Ioana Avram, Carla Maria Babe-Bolyai University Special Education Department Cluj-Napoca Romania Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills. © 2024 IEEE.

关键词： academic performance children digital devices

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Mobile Medical Assistant System for Laboratory Report Analysis and Medical Drug Identification 4

Mobile Medical Assistant System for Laboratory Report Analys...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Warnakulasuriya, Deshani Dewangi, Tharushi Sewwandi, Navodya Rathnayake, Minoli Kodagoda, Nuwan Suriyawansha, Kushanra Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month's results of the medical lab report of the patient using machine learning. © 2022 IEEE.

关键词： Hospitals

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Enhancing Content-Based Histopathology Image Retrieval Using QR Code Representation

Enhancing Content-Based Histopathology Image Retrieval Using...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rouzegar, Hamidreza Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Engineering Department St. CatharinesON Canada Laurier University Faculty of Science Wilfrid WaterlooON Canada

ISBN: (纸本)9781665430654

The growing field of Content-Based Medical Image Retrieval (CBMIR) plays an integral role in the diagnosis and treatment plan of numerous diseases, including cancer. However, the effective representation of gigapixel medical images via a large number of extracted features remains a challenging task, crucially influencing other processes in a digital workflow. In this paper, we propose a novel QR code representation strategy to enhance retrieval performance. Unlike the traditional one-dimensional binary representation methods (i.e., barcodes), this 2D approach captures more intricate and informative patterns from the features by differentiating each pair of features resulting in a 2D binary vector. We delve into three distinct QR code generation strategies, namely, the Thresholding QR, the MinMax QR, and the Hybrid QR, each offering unique strengths. Our experiments on representing whole slide images of the Cancer Genome Atlas (TCGA) dataset reveal that while the Hybrid QR tends to provide balanced performance, there are instances where the other two methods outshine. Even though this approach requires more memory usage, the considerable enhancement in accuracy justifies this trade-off;obviously, in medical applications, accuracy holds the highest priority. Hence, the findings indicate that QR codes can effectively improve the performance of CBMIR systems by not only accelerating the retrieval process but also increasing the accuracy of image retrieval, leading to potentially more accurate diagnoses and treatment planning. © 2023 IEEE.

关键词： Barcode Content-Based Image Retrieval Histopathology Images Medical Image Analysis QR Code

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Image Reconstruction Improvement of Variable Coded Aperture using Deep Learning Method for Gamma and Lensless Imaging Applications

Image Reconstruction Improvement of Variable Coded Aperture ...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Schwarz, Ariel Shemer, Amir Danan, Eliezer Cohen, Noa E. Danan, Yossef Department of Electrical and Electronics Engineering Azrieli College of Engineering Jerusalem9103501 Israel Faculty of Engineering Bar-Ilan University Ramat-Gan5290002 Israel School of Software Engineering and Computer Science Azrieli College of Engineering Jerusalem9103501 Israel

ISBN: (纸本)9798350345995

In gamma ray imaging for nuclear medicine, coded aperture is used to improve sensitivity. one of the main reconstructing methods is inverse filtering (deconvolution), where the recorded image is cross-correlated with periodic inverse filter of coded array. The reconstruction is free of coding noise for arbitrary array. However, amplification of quantum noise affect the reconstructed image. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks. Statistically and experimentally pinhole arrays have at least one term which is zero, resulting unacceptably noisy reconstruction. In our previous research we presented new approach of variable coded aperture (VCA) design for far and near field imaging applications [1-4]. The imaging system is based on time multiplexing method using variable multi pinhole array. The unique variable design enables to overcome the spatial frequencies cutoff and small terms in the Fourier transform exists in static multi pinhole array. The overall pinholes positions are designed to avoid spatial frequencies loss. However, traces of duplications can still be detected in reconstruction using Wiener filtering. Furthermore, since coded aperture blocks most of the photons that enter the detector, the dynamic range of the image is limited, thus leading to low contrast image and inadequate colors gamut. © 2023 IEEE.

关键词： Quantum noise

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Compact NSGA-II for Multi-objective Feature Selection

Compact NSGA-II for Multi-objective Feature Selection

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350337020

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In order to select optimal features, we have proposed a binary Compact NSGA-II (CNSGA-II) algorithm. Compactness represents the population as a probability distribution to enhance evolutionary algorithms not only to be more memory-efficient but also to reduce the number of fitness evaluations. Instead of holding two populations during the optimization process, our proposed method uses several Probability Vectors (PVs) to generate new individuals. Each PV efficiently explores a region of the search space to find non-dominated solutions instead of generating candidate solutions from a small population as is the common approach in most evolutionary algorithms. To the best of our knowledge, this is the first compact multi-objective algorithm proposed for feature selection. The reported results for expensive optimization cases with a limited budget on five datasets show that the CNSGA-II performs more efficiently than the well-known NSGA-II method in terms of the hypervolume (HV) performance metric requiring less memory. The proposed method and experimental results are explained and analyzed in detail. © 2023 IEEE.

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Improving performance in collaborative games through personality-based matchmaking 1

Improving performance in collaborative games through persona...

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Actas del I Congreso Espanol de Videojuegos, CEV 2022 - 1st Spanish Congress on Videogames, CEV 2022

作者： Villar, Alejandro León, Carlos Department of Software Engineering and Artificial Intelligence Computer Science Faculty Universidad Complutense de Madrid Madrid28040 Spain

Automatic player matchmaking in video games is usually focused on balancing players by taking into account their past performance. The objectives of video games in which the winning condition is clear and specifically defined by their design make this approach widely used and effective. However, this approach is not necessarily optimal when the main objective is not only winning but also obtaining other rewards like creative achievements, enjoyment, socialization or world construction. This paper describes a formal approach for automatic matchmaking that takes into account the players' personality traits. This form of matchmaking is oriented towards game genres in which competition is not necessarily the general dynamic. An implementation of the system, evaluation in a virtual escape room and results are provided. Preliminary results suggest that the approach is potentially effective. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Interactive computer graphics

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A-REA: Relation-Aware Graph Convolutional Network Entity Alignment Via a Differential Adjacency Matrix

A-REA: Relation-Aware Graph Convolutional Network Entity Ali...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Dang, Yuemei Qiu, Chen Gao, Feng Gu, Jinguang Zhang, Lihua Arman, Ghazaleh School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Software College East China Jiaotong University Jiangxi Nanchang China Urmia University of Technology Faculty of Information Technology and Computer Engineering Urmia Iran

ISBN: (纸本)9781665457033

Entity Alignment is the task of seeking entities with the same semantics from distinct knowledge graphs (KGs). Researchers have recently proposed Graph Convolutional Network (GCN) based approaches to achieve this task. Most existing GCN-based methods embed all the relations into an identical value, making the underlying loss on the diversity of the relationship. Relation semantics of entities and relation structure have benefitted the entity alignment task in the monolingual scenarios. This paper proposes a Relation-Aware Graph Convolutional Network with a Differential Adjacency Matrix (A-REA), which exploits the relation-specific semantics and structural information of entities in the KGs to promote the entity representations. The experiments on DBP-15K and DWY-100K datasets, including three cross-lingual subsets and two cross-knowledge graph sub-datasets, reveal that our approach leads to advance in entity alignment task in both the cross-lingual and cross-knowledge KGs. Discriminating the relation shows the effective signal for entity alignment, especially for the schema heterogeneity of different KGs. © 2023 IEEE.

关键词： Knowledge graph

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Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka 4

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Jayawardena, Aparna Ganegoda, Kasuni Imbulana, Sakuni Gunapala, Gavin Kodagoda, Nuwan Jayasinghe, Thilini Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries;leaf disease identification;cover crop identification;and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology. © 2022 IEEE.

关键词： Rubber

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