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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Kurniawan, Asep Oktivasari, Prihatin Fauzi, Nur Noviandri, Muhammad Iqbal, Muhammad Computer and Informatics Engineering Politeknik Negeri Jakarta Jakarta Indonesia

ISBN: (纸本)9798350345162

In the business process of a payment system using web applications, it requires a container as a place to run a program or what is commonly referred to as a server with services such as a web server, database server, and security system. This research aims to be able to provide cloud computing infrastructure for payment systems. With the aim of creating efficient, stable and secure server services. This research uses Google Cloud and Amazon Web Service as Load Balance providers for cloud computing, as well as using NginX for web server needs and cloudflare as a domain distribution and security system. The results of this study are information for designing a server based on cloud computing and can provide or prepare infrastructure needs for payment system needs, servers built with load balance are efficient and stable because they have a low failure value, and can accommodate up to 10,000 users with a user per connection ratio of 33.3 % and 100% can divert attacks sending Internet Control Message Protocol packets that lead to the Internet Protocol Server to the Internet Protocol Content Delivery Network service provider cloudflare. © 2023 IEEE.

关键词： cloud computing load balance payment system security system web server

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Influence Maximization in Dynamic Social Networks Under Partially Observable Environments

Influence Maximization in Dynamic Social Networks Under Part...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Smani, Gkolfo I. Megalooikonomou, Vasileios University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350327595

Influence and influence diffusion have been studied widely in social networks. Influence maximization is the problem of detecting a set of influential nodes in a social network, which represents relationships among individuals. Although, most of the existing works on this task focus on static networks, in this paper we study the problem of influence maximization in dynamic social networks where the changes that occur over time can be observed by periodically probing nodes to update their connections. The goal of this work is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose three algorithms, MaxC, MaxDC and MaxT, to approximate the optimal solution with probing nodes, which achieve improvement on estimating the number of activated nodes over state-of-the-art-method by 2.88%, 4.95% and 5.39%, respectively. © 2023 IEEE.

关键词： core number diffusion dynamic social networks graphs maximization influence probing social influence trussness

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Eye-Tracking Based Automatic Summarization of Lecture Slides 5

Eye-Tracking Based Automatic Summarization of Lecture Slides

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5th International Conference on computer and Applications, ICCA 2023

作者： Vazaios, Stylianos Mallas, Andreas Xenos, Michalis University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350303254

This paper presents a novel approach to automatically summarize lecture slides using eye-tracking data. The tool generates personalized summaries by analyzing learners' visual attention patterns, with the aim of reducing information overload and enhancing comprehension. While existing text summarization methods often overlook individual preferences and attention patterns, our proposed approach considers all slide elements, including text and visuals. It dynamically selects areas of interest based on learners' fixation points. The tool is developed as a web application, enabling users to access presentations, receive personalized summaries, and save them for optimized studying. It incorporates features such as administration and settings, slides uploading, and summary viewing. To evaluate the tool, we conducted an experiment involving 62 participants to assess usability and satisfaction with the automated summaries. The evaluation results indicated positive feedback in terms of ease of use and visual appeal. Participants expressed satisfaction with the generated summaries and were given the option to select their preferred summarization percentage. Our analysis revealed that summarization preferences are not related to prior knowledge. Overall, this tool demonstrates the potential of personalized summarization based on eye-tracking data, thereby enhancing learning experiences. © 2023 IEEE.

关键词： Eye tracking

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Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data 4

Topic Modeling Using Latent Dirichlet Allocation Method Base...

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4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Fernandis, Rasio Swanjaya, Daniel Ramadhani, Risky Aswi Kasih, Patmi Sahertian, Julian Universitas Nusantara Pgri Kediri Faculty of Engineering and Computer Science Dept. Informatics Engineering Kediri Indonesia

ISBN: (纸本)9798350376111

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children's development at school through the results of each semester's assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children's anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children's anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children's anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children's anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes. © 2024 IEEE.

关键词： anecdotal record coherence score kindergarten Latent Dirichlet Allocation topic modeling

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Development of 2D Animation for Diarrhea Disease Based on computer Generated Imagery (CGI) 8

Development of 2D Animation for Diarrhea Disease Based on Co...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Marcheta, Noorlela Matin, Iik Muhamad Malik Nazruli, Rifqi Choiru Sonjaya, Iwan Rosalina, Mira Politeknik Negeri Jakarta Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350344691

Diarrhea is one of the biggest diseases in the world that can cause death. Diarrhea can be caused by poor environmental sanitation or poor lifestyle habits. Prevention can be done early, such as education at the primary education level to reduce the high number of diarrheal diseases. However, the education provided is still minimal. This is caused by the educational media which still uses books and blackboards, thus affecting students' understanding of diarrheal disease so that it is less than optimal in preventing diarrheal disease. Animation technology is one example of learning media development. Through animation, students become more interested and teachers become more helpful in communicating diarrheal disease material. In this paper, animation development is carried out. We develop 2D video animation using computer Generated Imagery techniques. The results of beta testing via a questionnaire administered to respondents showed that 98.43% of respondents agreed that animated videos could be used as a learning medium for diarrheal disease material. © 2023 IEEE.

关键词： Animation

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Unlocking the Secrets of Student Success in Low-Code Platforms: An In-Depth Comparative Analysis

Unlocking the Secrets of Student Success in Low-Code Platfor...

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2024 ASEE Annual Conference and Exposition

作者： Tsakalerou, Mariza Evangelou, Semira Maria Xenos, Michalis School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Computer Engineering and Informatics Department University of Patras Greece

This paper presents a comprehensive exploration of students' experiences with low-code platforms, which emphasize high-level logic and functionality over intricate coding. This is achieved by utilizing pre-test and post-test questionnaires, Mendix Studio Pro as an exemplar of such platforms, and structured experimentation tasks. The pre-test captures participants' baseline perspectives, while the post-test examines shifts after hands-on interaction. The study addresses research questions concerning demographic correlations, coding backgrounds, attitude changes, coding performance, and qualitative insights. The experiment protocol includes an instructional video, demographic surveys, and three tasks. Descriptive statistics and statistical tests provide insights. Performance discrepancies between IT and non-IT backgrounds are statistically significant. Feedback indicates positive perceptions of low code. © American Society for engineering Education, 2024.

关键词： Students

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A Procedure for Optimization of Placement Products in Large Industrial Areas of a Greek Soft Drinks Company

A Procedure for Optimization of Placement Products in Large ...

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2024 International Conference on Applied Mathematics and computer Science, ICAMCS 2024

作者： Tsakiridis, Sotirios Tsimpiris, Alkiviadis Angeioplastis, Athanasios Papaioannou, Nikolaos Mastorocostas, Paris Papakonstantinou, Apostolos Kamilali, Angeliki Varsamis, Dimitrios International Hellenic University Dept. Computer Informatics and Telecommunications Engineering Serres Greece University of West Attica Dept. Informatics and Computer Engineering Egaleo Greece Cyprus University of Technology Dept Civil Engineering and Geomatics Lemesos Cyprus

ISBN: (纸本)9798350363807

In this paper we present the process of optimal placement of industrial products (pallets) in mapped ware-houses and specifically in a soft drink company in Greece. The optimization criterion is to minimize the cost of moving the lifting machine that places the products in specific positions. The computational method of optimal placement of industrial products receives data from the automatic product count of the mapped warehouse and from the sales predictive model. Therefore, the computational method, having knowledge of the location and number of products in the warehouse and the prediction of sales for each product, places the products produced in an optimal way. Automatic product counting is carried out using an unmanned aerial vehicle (UAV) which developed and applied in soft drink company in Greece. The sales prediction is carried out through a prediction model which developed in a soft drink company in Greece. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Ensemble-Based Machine Learning Approach to Identify SARS-CoV-2 Virus Infection by Analyzing S Protein Sequences 2nd

An Ensemble-Based Machine Learning Approach to Identify SARS...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Moni, Raka Zahid Hasan, Md. Shahriar Shakil, Md. Ferdous, Most. Jannatul Arefin, Mohammad Shamsul Bhuiyan, Touhid Health Informatics Research Lab Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9789819989362

Coronavirus disease (COVID-19), an effect of the SARS-CoV-2 virus, is an emerging infectious disease that infects humans due to interspecies transmission. Typically, Spike (S) protein plays a crucial role in entering host cells and beginning infection in contrast to the Membrane (M) and Envelope (E) proteins which are primarily involved in virus assembly. The purpose of this research is to examine the S protein sequences of the human virus to forecast SARS-CoV-2 infection potential. Using computational modeling and bioinformatics approaches, we analyzed the dataset taken from the Virus Pathogen Database except for 47 SARS2-CoV sequences from the SARS2-CoV Database containing aligned sequences of lengths 2396. This study suggests using several machine learning methods to determine infectivity based on the sequence of the coronavirus spike protein. The classical machine learning approach such as k-nearest neighbors (KNNs), Gaussian Naive Bayes (GaussianNB), support vector machine (SVM), decision tree (DT) is used. Bagging Classifier, Gradient Boosting algorithm, and Random Forest are used as the ensemble machine learning algorithm. Artificial Neural Network (ANN) is also applied as a deep learning model. The Gradient Boosting algorithm demonstrates the best accuracy in determining infectivity status. According to our research, the outcome of this study has a chance to accurately forecast coronavirus infectivity from genetic information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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Sentiment Analysis with the Use of Transformers and BERT 14

Sentiment Analysis with the Use of Transformers and BERT

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Douka, Elisavet Perikos, Isidoros Hatzilygeroudis, Ioannis University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350318067

Recognizing the emotional content of natural language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. The field of emotion recognition occupies an important place in the applications of artificial intelligence. The rapid increase in the popularity of social media has created the need to study and document their use. In this paper, both classical classification algorithms and various neural network architectures were tested. In the context of this paper, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. © 2023 IEEE.

关键词： Deep learning

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Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

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computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： computer vision deep learning gait recognition sign language recognition machine learning

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