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5th International Conference on computer and Applications, ICCA 2023

作者： Khalaf, Mena Nagy A. Soliman, Taysir Hassan A. Mohamed, Sara Salah Assiut University Faculty of Computer and Information Information System Department Assiut Egypt New Valley University Faculty of Science Mathematics and Computer Science Department New Valley Egypt

ISBN: (纸本)9798350303254

The tertiary structures of proteins play a critical role in determining their functions, interactions, and bonding in molecular chemistry. Proteins are known to demonstrate natural dynamism under various physiological conditions, which enables them to adjust their tertiary structures and effectively interact with the surrounding molecules. The present study utilized the remarkable progress made in Generative Adversarial Networks (GANs) to generate tertiary structures that accurately mimic the inherent attributes of actual proteins, which includes the backbone conformation as well as the local and distal characteristics of proteins. The current study has introduced a robust model, ROD-WGAN hybrid, that is able to generate large-scale tertiary protein structures that greatly mimic those found in nature. We have made several noteworthy contributions in pursuit of this objective by integrating the ROD-WGAN model with a hybrid function that incorporates adversarial loss, perceptual loss, distance-by-distance loss, and structural similarity loss. Through this innovative approach, we achieved remarkable results, particularly in the generation of protein structures of considerable length. The ROD-WGAN hybrid model has transcended the limitations inherent in the ROD-WGAN model, demonstrating its capability in the successful synthesis of high-quality proteins, characterized by a length of up to 256 amino acids. This accomplishment serves as a demonstrating the effectiveness and potential of our proposed methodologies. Furthermore, these generated protein structures serve as a valuable resource for data augmentation in crucial applications such as molecular structure prediction, inpainting, dynamics, and drug design. Interested individuals can access the data, code, and trained models at https://***01/ROD-WGAN-and-ROD-WGAN-hybird-models. © 2023 IEEE.

关键词： Proteins

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2023 International Conference on computer science, information Technology and Engineering, ICCoSITE 2023

作者： Rani, Larissa Navia Yuhandri, Yuhandri Universitas Putra Indonesia YPTK Information System Computer Science Faculty Padang Indonesia Universitas Putra Indonesia YPTK Information Technology Computer Science Faculty Padang Indonesia

ISBN: (纸本)9798350320954

In this study aimed to measure the level of similarity between two logos, both those that look different and those that look the same. This can be realized by forming a logo image database that is stored in a logo image database derived from various existing logo image data. This research uses 4 logo images as data testing and 210 image data for the database as data training. All of the logo images used come from the Ministry of Law and Human Rights of the Republic of Indonesia (Kemenkumham RI) West Sumatra Regional Office. The size images are color images with a pixel size of 320 x 320 pixels, the purpose of which is for the process of dimensional uniformity of the images to be studied. This research uses Content Base Image Retrieval (CBIR) method to search for images from a large image database than using Convolutional Neural Network (CNN) type Residual Network (ResNet-18) Architecture to get the similarity score accurately. The result of this implementation is the formation of an automatic distribution of training images and validation images with 147 training image data values (70%) and 63 validation images (30%) of the 210 existing images. The result of this research is producing the algorithm to implement the method and the tool software application to measure the similarity of logo images. The accuracy of this tool is 93.65% with a total of 84 iterations. © 2023 IEEE.

关键词： Network architecture

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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Enhancing Children's Health Awareness through Narrative Game-Based Learning using Digital Technology: A Framework and Methodology 9

Enhancing Children's Health Awareness through Narrative Game...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Muniandy, Nithiyaa Kamsin, Amirrudin Sabri, Mas Idayu Faculty of Computer Science & Information Technology University Malaya Kuala Lumpur Department of Computer System & Technology Malaysia

ISBN: (纸本)9798350304084

Effective health education and awareness initiatives are essential in a world facing growing concerns about children's well-being and rising unhealthy behaviours. This research presents a Narrative Game-Based Learning (N-GBL) framework designed to enhance children's health awareness, addressing critical gaps in their health education. Through a systematic literature review following the PRISMA methodology, this study identifies 13 essential components that form the foundation of the N-GBL framework. These components are structured around the Input-Process-Output (IPO) model, emphasizing narratives, game mechanics, and user engagement strategies. The core purpose of the N-GBL framework is to deepen children's understanding of the consequences of their choices, fostering informed decision-making and to empower them to make healthier choices. This research contributes to developing effective health awareness games tailored to children's learning preferences, bridging existing gaps in their health education. The N-GBL framework offers a revolutionary approach to health education for children, significantly enhancing health awareness and reducing health issues resulting from unhealthy behaviours. Future work will involve expert validation and practical testing in primary schools to transform health education for children. © 2023 IEEE.

关键词： Decision making

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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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User Interface Rendering Time in Android Applications: Revealing the Effects of Design Patterns 23

User Interface Rendering Time in Android Applications: Revea...

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8th International Conference on Sustainable information Engineering and Technology, SIET 2023

作者： Paramadani, Rizky Bagus Akbar, Muhammad Aminul Pinandito, Aryo Informatics Engineering Departement Faculty of Computer Science Universitas Brawijaya Malang Indonesia Information System Department Universitas Brawijaya Faculty of Computer Science Indonesia

ISBN: (纸本)9798400708503

The Google Play platform boasts a total of 2,597,819 applications. A pivotal gauge of an application's success rests on its practical utility in daily life, coupled with its demonstrated strong performance metrics. There are several performance metrics to evaluate the performance of an application, such as RAM usage, CPU utilization, and their impact on battery life. Additionally, it is crucial to consider the User Interface (UI) factor, which serves as the primary connection between users and applications. Beyond its role as a bridge between users and applications, the UI can also significantly affect the performance of an application, as rendering a UI often takes a considerable amount of time. Various approaches can be employed to enhance the rendering time of a UI, one of which involves the use of design patterns like Model-View-Controller (MVC) and Model-View-ViewModel (MVVM), which effectively separate the business logic from the UI layer. This research study compares the performance of these two design patterns in the context of UI rendering time. The findings of this study confirm that MVVM outperforms MVC significantly in rendering UI components more rapidly. This superiority is attributed to MVVM's inherent ability to isolate business logic, ultimately reducing rendering delays. Furthermore, MVVM's prominent data binding feature further strengthens its efficiency in terms of UI rendering time. © 2023 ACM.

关键词： User interfaces

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Sentiment Analysis on Tourism Place using Naive Bayes 17

Sentiment Analysis on Tourism Place using Naive Bayes

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17th International Conference on Telecommunication systems, Services, and Applications, TSSA 2023

作者： Atmadja, Aldy Rialdy Rahmawati, Aprilia Alam, Cecep Nurul Dauni, Popon Saputra, Yogi Faculty of Science and Technology UIN Sunan Gunung Djati Informatics Department Bandung Indonesia Faculty of Computer Science and Information System Universitas Kebangsaan RI Information System Department Bandung Indonesia

ISBN: (纸本)9798350309164

The method of extracting information from a text is called text mining. In contrast, sentiment analysis involves examining text data to determine whether a statement expresses good or negative conversations. In this study, a system was developed that categorizes tweets about West Bandung Regency tourism spots that contain both favorable and negative remarks. The Cross Industry Standard Process for Data Mining (CRISP-DM) technique is used to carry out the classification process utilizing the Naive Bayes algorithm. You will learn how locals feel about West Bandung Regency's tourist attractions through this method. Six scenarios were developed using the categorization findings from 250 pieces of data that included both positive and negative sentiment. The scenario has got the greatest f1-score of 82.8%. These findings demonstrate how effectively the Naive Bayes algorithm performs sentiment analysis. © 2023 IEEE.

关键词： Tourism

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Facial Emotion Analysis for Evaluating the Suitability of Online Learning Modules: A Case Study on a MERN-Based Platform 10

Facial Emotion Analysis for Evaluating the Suitability of On...

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10th International Conference on Education and Technology, ICET 2024

作者： Wardaya, Utara Setya Bachtiar, Fitra Abdurrachman Pradana, Fajar Marutschke, Daniel Moritz Brawijaya University Faculty of Computer Science Information System Department Malang Indonesia Brawijaya University Faculty of Computer Science Informatics Department Malang Indonesia Kyoto University of Advanced Science Faculty of Economics and Business Administration Kyoto Japan

ISBN: (纸本)9798331522483

Learning is a process that involves the progressive transfer of knowledge, encompassing both the physical and psychological components of the learner. Emotions play a crucial role in the success of learning, yet they are often overlooked in the context of online education. Facial emotion detection technology and emotion analysis offer a solution for effectively recognizing and accommodating students' emotions in digital learning environments. The use of a Learning Management system (LMS) based on the MERN architecture (MongoDB, ***, ***, ***) is also critical in supporting the flexibility and responsiveness of online learning platforms. This research employed the Agile model, which was systematically planned and organized. The Agile model is an approach that allows for continuous adjustments during development. The aim of this study is to integrate artificial intelligence into an e-learning system to detect and evaluate students' emotions, thereby enabling the development of more effective online learning methods. The design process following the Agile Method involved needs analysis, modeling with UML (Use Case, Activity Diagram, Sequence Diagram, and Class Diagram), as well as database and code implementation. The backend development focused on database management, server configuration, and API integration, while the frontend was designed for a responsive user interface. Testing was conducted using Black Box and White Box methods, which demonstrated 100% validity for system functionality, while user feedback through UAT yielded a 94% validity score. The results indicate that emotion detection functions effectively in e-learning, and emotion analysis can enhance the quality of learning, with positive emotional states leading to better outcomes. © 2024 IEEE.

关键词： Black-box testing

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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