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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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Advancing Mixed Reality Game Development: An Evaluation of a Visual Game Analytics Tool in Action-Adventure and FPS Genres

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Proceedings of the ACM on Human-computer Interaction 2024年第CHI PLAY期8卷 1-32页

作者： Sargolzaei, Parisa Rastogi, Mudit Zaman, Loutfouz Faculty of Business and Information Technology Ontario Tech University 2000 Simcoe St N OshawaONL1G 0C5 Canada Computer Science and Technology SRM Institute of Science and Technology SRM Nagar Chengalpattu District Tamil Nadu Kattankulathur603203 India

In response to the unique challenges of Mixed Reality (MR) game development, we developed GAMR, an analytics tool specifically designed for MR games. GAMR aims to assist developers in identifying and resolving gameplay issues effectively. It features reconstructed gameplay sessions, heatmaps for data visualization, a comprehensive annotation system, and advanced tracking for hands, camera, input, and audio, providing in-depth insights for nuanced game analysis. To evaluate GAMR's effectiveness, we conducted an experimental study with game development students across two game genres: action-adventure and first-person shooter (FPS). The participants used GAMR and provided feedback on its utility. The results showed a significant positive impact of GAMR in both genres, particularly in action-adventure games. This study demonstrates GAMR's effectiveness in MR game development and suggests its potential to influence future MR game analytics, addressing the specific needs of developers in this evolving area. © 2024 ACM.

关键词： Visual analytics

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High-Performance Intelligent system for Real-Time Medical Image Using Deep Learning and Augmented Reality 4th

High-Performance Intelligent System for Real-Time Medical Im...

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4th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Image-Based Automatic Energy Meter Reading Using Deep Learning

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computers, Materials & Continua 2023年第1期74卷 203-216页

作者： Muhammad Imran Hafeez Anwar Muhammad Tufail Abdullah Khan Murad Khan Dzati Athiar Ramli Institute of Computer Sciences and Information Technology Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock CityPakistan Kuwait College of Science and Technology Kuwait CityKuwait School of Electrical&Electronic Engineering USM Engineering CampusUniversiti Sains MalaysiaPenang14300Malaysia

We propose to perform an image-based framework for electrical energy meter *** aim is to extract the image region that depicts the digits and then recognize them to record the consumed *** the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time ***,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by *** this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and *** image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image *** training and evaluation,the image dataset is annotated to produce the ground truth of all the ***,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the *** proves to be robust against the mentioned image variations compared with the traditional handcrafted *** proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

关键词： Convolutional neural network object localization machine learning

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Text Prediction Zero Probability Problem Handling with N-gram Model and Laplace Smoothing

Text Prediction Zero Probability Problem Handling with N-gra...

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2022 International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2022

作者： Mimi, Raonak Jahan Masud, Md Abdul Rahman, Rifat Dina, Nusrat Sultana Patuakhali Science and Technology University Faculty of Computer Science and Engineering Bangladesh Patuakhali Science and Technology University Department of Computer Science and Information Technology Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

In Natural Language Processing, text prediction represents the process of predicting the word with the highest probability through a predictive language model from a series of text corpus. The N-gram model is familiar and considered the handiest and most computationally cost-effective model for text processing. Additionally, higher N-gram models, especially the 5-gram ones, give the best text prediction. Interestingly, these better prediction results were obtained only on the training dataset. In contrast, the highest N-gram model imploded badly on the evaluation dataset. This paper proposes an approach where the N-gram model, especially the bi-gram model, and the fine tuning with Laplace Smoothing, provide the best prediction results at the evaluation stage. © 2022 IEEE.

关键词： Text processing

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Beyond Boundaries:Strengthening the Fabric of IoT Security through Blockchain Innovations 18

Beyond Boundaries:Strengthening the Fabric of IoT Security t...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Nisa, Khair Ul Alqahtani, Hanan Saad Fatima, Ummatul Lhioui, Chahira Helali, Rasha Gaffer Mohammed Hussain, Gousiya University of Bisha Department of Computer Science and Information Technology Saudi Arabia King Khalid University Department of Computer Saudi Arabia Aligarh Muslim University Faculty of Science Department of Statistics and Operations Research Aligarh India Mewar University Department of Computer Sciences Chittorgarh India

ISBN: (纸本)9789380544519

Blockchain technology has been utilized to bypass regulations on IoT security. Blockchain technology provides numerous benefits that can improve the security of Internet of Things devices with limited resources, such as decentralization, shared resources, immutable data transfer, dispersed network topology, and shared resources. The study delves into the advantages of blockchain technology for identity management, authentication, data integrity, availability, and confidentiality. The research findings have improved the accessibility of this study. This investigation has revealed research on using blockchain technology to address cybersecurity issues. This study has focused solely on the subject of cyber security. The advantages of blockchain technology are clear. Here is a point of concern due to the system's instability and decentralization, which limits its capacity to tackle potential problems in the information security field. The increase in blockchain applications has enhanced compliance with current security protocols and discouraged cyber criminals from attempting security breaches. The study's findings may inspire further exploration of cybersecurity in fields beyond those closely linked to the Internet of Things. Ensuring the integrity of the cryptography and certification processes is vital, especially as end users depend on encryption for various daily tasks, especially with the increasing use of HTTPS encryption on the internet. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain Cybersecurity Internet of Things (IoT)

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Cloud computing-enabled IIOT system for neurosurgical simulation using augmented reality data acces

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Digital Communications and Networks 2023年第2期9卷 347-357页

作者： Jun Liu Kai Qian Zhibao Qin Mohammad Dahman Alshehri Qiong Li Yonghang Tai Yunnan Key Laboratory of Opto-electronic Information Technology Yunnan Normal UniversityKunming650500China Department of Thoracic Surgery Institute of The First People's Hospital of Yunnan ProvinceKunmingChina Faculty of Life and Biotechnology Kunming University of Science and TechnologyKunmingChina Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

In recent years,statistics have indicated that the number of patients with malignant brain tumors has increased ***,most surgeons still perform surgical training using the traditional autopsy and prosthesis model,which encounters many problems,such as insufficient corpse resources,low efficiency,and high *** the advent of the 5G era,a wide range of Industrial Internet of Things(IIOT)applications have been *** Reality(VR)and Augmented Reality(AR)technologies that emerged with 5G are developing rapidly for intelligent medical *** address the challenges encountered during neurosurgery training,and combining with cloud computing,in this paper,a highly immersive AR-based brain tumor neurosurgery remote collaborative virtual surgery training system is developed,in which a VR simulator is *** system enables real-time remote surgery training interaction through 5G *** experts and 18 novices were invited to participate in the experiment to verify the ***,the two simulators were evaluated using face and construction validation *** results obtained by training the novices 50 times were further analyzed using the Learning Curve-Cumulative Sum(LC-CUSUM)evaluation method to validate the effectiveness of the two *** results of the face and content validation demonstrated that the AR simulator in the system was superior to the VR simulator in terms of vision and scene authenticity,and had a better effect on the improvement of surgical ***,the surgical training scheme proposed in this paper is effective,and the remote collaborative training effect of the system is ideal.

关键词： Neurosurgery IIOT Cloud computing Intelligent medical 5G AR

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Intense Triad Defender for End-User Security in Cyber Physical system 2

Intense Triad Defender for End-User Security in Cyber Physic...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Sheryl Sharon, G. Maranco, M. Nidhya, R. Sivakumar, M. Coimbatore Institute of Technology Department of Computer Science and Engineering Coimbatore641014 India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

The rapid advancement of technology has undoubtedly brought comfort to humanity, but it also necessitates robust authentication measures to ensure security in the ever-expanding e-world. This research aims to address the critical aspects of security, focusing on end-user authentication. The proposed solution incorporates multiple factors, including location-based authentication, text-based authentication with enhanced encryption standards, and facial recognition authentication. Location-based authentication serves to restrict access geographically, mitigating the risk of impersonation by enforcing strict geographic limits on facility access. Strong text passwords, utilizing a unique algorithm with a classic salt method, add an extra layer of complexity for potential attackers, making it challenging to compromise user credentials. The incorporation of facial recognition technology acts as a deterrent against non-human entities attempting unauthorized access. Leveraging deep learning and API servers ensures precision and scalability in the model, enhancing its effectiveness in real-world scenarios. These multi-factor authentication techniques collectively contribute to a secure, reliable, and forward-looking solution to address the evolving landscape of digital security threats. In adhering to the age-old adage "prevention is better than cure,"the research emphasizes the importance of proactive measures to safeguard against potential security breaches. © 2024 IEEE.

关键词： Network security

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L1Norm Minimization in ISAR Imaging 23

L1Norm Minimization in ISAR Imaging

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Lazarov, Andon Minchev, Dimitar Nikola Vaptzarov Naval Academy Information Technology Department Varna Bulgaria Burgas Free University Computer Science and Engineering Faculty Burgas Bulgaria

ISBN: (纸本)9798350382365

This work deals with an ISAR (Inverse Synthetic Aperture Radar) image enhancement algorithm using target's sparse image and image's l1 norm minimization. As calculation of l1 norm of two-dimensional (2D) image matrix is too complicated, target's image matrix is resized to one-dimensional (1D) one. According to ISAR theory, the ISAR signal matrix is a Fourier transform of the target's image matrix. Thus, the ISAR signal is obtained one dimensional one while modelling by Fourier transform of a certain target's image column matrix. In the image reconstruction algorithm, 12 norm constrained function and l12 objective function are defined. Simulation experimental results prove functionality of the developed algorithm. © 2024 IEEE.

关键词： Radar imaging

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Expert Phishing Detection system

Expert Phishing Detection System

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Alsarhan, Ayoub Al-Aiash, Issa Al-Fraihat, Dimah Aljaidi, Mohammad Abu Laila, Dena Abd Al Hakem Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (纸本)9798350353464

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model’s performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts. ©2024 IEEE.

关键词： Cyber-security Phishing attacks Phishing URLs

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