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15th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Mahyoub, Mohamed Abdulhussain, Sadiq H. Natalia, Friska Sudirman, Sud Mahmmod, Basheera M. Tutor Reach United Kingdom University of Baghdad Baghdad Iraq Universitas Multimedia Nusantara Faculty of Engineering and Informatics Tangerang Indonesia School of Computer Science and Mathematics Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9798350335149

Pattern is very commonly used in the textile and fashion industry. Pattern design is an area where designers need to come up with new and attractive patterns every day. It is very difficult to find employees with a sufficient creative mindset and the necessary skills to come up with new unseen attractive designs. Therefore, it would be ideal to identify a process that would allow for these patterns to be generated on their own with little to no human interaction. This can be achieved using deep learning models and techniques. One of the most recent and promising tools to solve this type of problem is Generative Adversarial Networks (GANs). In this paper, we investigate the suitability of GAN in producing abstract patterns. We achieve this by generating abstract design patterns using the two most popular GANs, namely Deep Convolutional GAN and Wasserstein GAN. By identifying the best-performing model after training using hyperparameter optimization and generating some output patterns we show that Wasserstein GAN is superior to Deep Convolutional GAN. © 2023 IEEE.

关键词： Generative adversarial networks

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Multi-talker Verbal Interaction for Humanoid Robots

Multi-talker Verbal Interaction for Humanoid Robots

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Bartlomiej Klin Ryszard Beniak Michal Podpora Arkadiusz Gardecki Joanna Rut Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Department of Computer Science Opole University of Technology Faculty of Production Engineering and Logistics Opole University of Technology

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

Working in multi-talker mode is viable under certain conditions, such as the fusion of audio and video stimuli along with smart adaptive beamforming of received audio signals. In this article, the authors verify part of the researched novel framework, which focuses on adapting to dynamic interlocutor’s location changes in the engagement zone of humanoid robots during the multi-talker conversation. After evaluating the framework, the authors confirm the necessity of a complementary and independent method of increasing the interlocutor’s signal isolation accuracy. It is necessary when video analysis performance plummets. The authors described the leading cause as insufficient performance during dynamic conversations. The video analysis cannot derive a new configuration when the interlocutor’s speech apparatus moves beyond the expected margin and the video frame rate drops.

关键词： Automation Accuracy Array signal processing Humanoid robots Oral communication Human factors

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Hybrid Convtranslstm for Spatio-Temporal Classification: Identifying Early Parkinson's Disease from Gait Patterns

SSRN

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SSRN 2025年

作者： Mahali, Muhammad Izzuddin Avian, Cries Putro, Nur Achmad Sulistyo Prakosa, Setya Widyawan Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Department Electrical Engineering Faculty of Engineering Universitas Brawijaya Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that affects motor function and impacts millions worldwide. Early diagnosis and precise staging of PD are critical for effective management and timely intervention. Traditional gait analysis focuses on lower limb movement to differentiate between moderate and advanced PD stages;however, integrating upper and lower limb dynamics offers a more comprehensive approach to distinguishing early-stage PD from *** study introduces a novel spatio-temporal model that integrates CNN, Transformer, and BiLSTM layers to classify PD based on the Hoehn and Yahr (H&Y) score. The architecture captures both spatial and temporal dependencies in gait patterns, with a CNN serving as the primary spatial feature extractor, utilizing a temporal-focused kernel, and augmented by a Transformer to enhance temporal feature learning. Additionally, a BiLSTM module is incorporated to effectively capture complex spatio-temporal gait dynamics across all modules. For data augmentation, sliding windows of 500, 800, and 1200 samples were employed, and the model's performance was rigorously assessed through 3-fold and 5-fold cross-subject *** included accuracy, F1-score, recall, precision, and a voting-based confidence score, while t-SNE visualization provided insights into spatio-temporal feature differentiation. The model demonstrated high performance, achieving optimal accuracy of 0.90 and an average confidence score of 0.97 with a 1200-sample window in 5-fold validation. These findings highlight the potential of a spatio-temporal approach incorporating upper and lower limb characteristics in gait analysis as a robust method for PD multi-class classification, supporting early and effective PD diagnosis from raw IMU signals. © 2025, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Root-zone Soil Moisture Nowcasting using Context Aware Machine Learning

Root-zone Soil Moisture Nowcasting using Context Aware Machi...

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International Conference on Agro-Geoinformatics

作者： Ayda F. Aktas Burak Berk Üstündağ Informatics Institute Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Estimation of root-zone soil moisture (SM) is crucial for effective agricultural management and water resource planning. However, current methods for soil moisture estimation exhibit several limitations that hinder their practical application. This study introduces a novel nowcasting model, which integrates in-situ and remote sensing data through a Predictive Error Compensated wavelet Neural NETwork (PECNET), addressing the drawbacks of existing *** SM estimation techniques often suffer from limited accuracy, inadequate contextual information, and no real-time monitoring capabilities. Although remote sensing technologies offer promising advantages, such as wide spatial coverage and frequent data acquisition, they are not immune to limitations. Vegetation coverage and density present challenges in accurately estimating root-zone SM using remote sensing techniques. These factors can introduce uncertainties and errors in the estimation process, thereby impacting the reliability of the *** overcome these limitations and enhance the accuracy of root-zone SM estimation, this study proposes the integration of remote sensing data with in-situ measurements. Specifically, Normalized Difference Vegetation Index (NDVI) calculations from Landsat 7 and Landsat 8 satellites are fused with evapotranspiration and rainfall data obtained from agrometeorological stations. Combining these datasets generates an 8-day time series for the target parcels, leveraging the contextual information provided by NDVI and seasonality to improve the accuracy of root-zone soil moisture *** develop a robust and efficient model, we introduce PECNET, which ensures the orthogonality of input features and facilitates the learning of non-linear relationships between variables. Notably, PECNET addresses the challenge of limited labeled training data, minimizing the risk of overfitting and enabling accurate estimation with fewer labeled samples. In addition, this study employs

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Predicting Career Trajectories of Information System Graduates Using AdaBoost Algorithm 9

Predicting Career Trajectories of Information System Graduat...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Kurniawan, Rahmad Gusti, Siska Kurnia Sukamto Syamsudhuha Abdillah, Rahmad Universitas Riau Faculty of Mathematics and Natural Sciences Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Universitas Riau Faculty of Mathematics and Natural Sciences Department of Mathematics Pekanbaru Indonesia

ISBN: (纸本)9798350305029

The undergraduate unemployment rate in Indonesia is increasing, necessitating universities to produce quality graduates. In alignment with global technological advancements, universities are committed to enhancing the quality of their graduates through curriculum evaluation. In this study, we investigated the Department of Information Systems at the University of Riau and developed an alum career analysis system using the AdaBoost algorithm. The data used in the study was collected via an online survey from graduates. Relevant features were selected from features to preprocess the data. The experiments conducted using the SAMME.R boosting method, with a learning rate of 0.1, 100 estimators, and cross-validation on the five-fold sampling method, resulted in the highest accuracy of 83.3%. The decision tree of the AdaBoost algorithm explains the factors influencing alums to pursue careers in their field, with English proficiency, learning methods, alum cohorts, and alum's activeness in job searching being the main determinants of success in their careers. The alum career analysis system proved reliable for evaluating the curriculum and producing quality graduates in line with technological advancements. Future work includes expanding the system to include graduates from other departments and implementing findings to improve the curriculum. © 2023 IEEE.

关键词： Curricula

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Logo Detection Plagiarism Using Generative Adversarial Network

Logo Detection Plagiarism Using Generative Adversarial Netwo...

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International Conference on informatics and Computing (ICIC)

作者： Rizky Rivanni Elviawaty Muisa Zamzami Rahmat Widia Sembiring Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Logo, as a graphic form and visual representation of a company, organization, or brand, has been widely utilized and disseminated across the internet. This accessibility makes logo easily replicable, allowing for the imitation and reuse of concepts or forms for creating new logo. Therefore, it is required to have a system that is able to analyze and identify the similarity between logos. In this study, logo plagiarism detection is proposed by using Generative Adversarial Network based on CycleGAN approach. The logo training process involves generator and discriminator networks as part of the GAN process to recognize the logos characteristics. The trained GAN model is tested by generating logo images using the model weights and inputting comparison logos to test their similarity through Template Matching method for plagiarism detection. From the generated test logo results, the logo produced by the generator closely resemble the original logo. The training results using GAN based on CycleGAN approach yielded average loss value of Generator A 6.3572, Generator B 6.1541, Discriminator A 0.6378 and Discriminator B 0.7186. The lowest logo plagiarism analysis value was obtained with an accuracy of 20.11% for logos with gradual changes in shape and different perspectives. The highest accuracy of 93.40% was achieved for logos with no changes at all.

关键词： Training Visualization Accuracy Shape Plagiarism Companies Generative adversarial networks Generators Character recognition informatics

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Hybrid Models for Recognizing Indonesian Textual Entailment 7

Hybrid Models for Recognizing Indonesian Textual Entailment

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7th International Conference on informatics and Computational Sciences, ICICoS 2024

作者： Ahmadian, Hendri Abidin, Taufik Fuadi Riza, Hammam Muchtar, Kahlil Universitas Syiah Kuala Graduate School of Mathematics and Applied Sciences Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia National Research and Innovation Agency Research Center for Artificial Intelligence and Cyber Security Jakarta Indonesia Universitas Syiah Kuala Faculty of Engineering Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350375886

A paragraph raises pertinent issues about linguistic disparities in understanding the semantic relationships between sentences within the natural language processing domain (NLP) Addressing this challenge necessitates the development of robust NLP models, with a specific emphasis on text entailment. This research endeavors to enhance the performance of Indonesian NLP benchmarks in text entailment by harnessing a variant of the Bidirectional Encoder Representations from Transformer (BERT) model tailored explicitly for the Indonesian language, known as IndoBERT. The approach in this study adopts a hybrid methodology, amalgamating the IndoBERT layer modification model by summing the last four hidden layers with neural networks. The performance evaluation of the hybrid models, combining the IndoBERT layer modification model with the Bidirectional Gated Recurrent Unit (BiGRU) model and the IndoBERT layer modification model with the Bidirectional Long Short-Term Memory (BiLSTM) model, yields the F1-scores of 0.87 and 0.84, respectively. © 2024 IEEE.

关键词： Semantics

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Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of Science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings International Conference on Software engineering and ACM International Conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (Science and Information Organization). All Rights Res

关键词： Software design

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Extreme Learning Machine for Parking Slot Recognition in Smart Parking System 8

Extreme Learning Machine for Parking Slot Recognition in Sma...

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8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Rahmat, Romi Fadillah Faza, Sharfina Syahputra, Ilham Nainggolan, Pauzi Ibrahim Sulaiman, Riza Rahman, Mohd Amiruddin Abd Universitas Sumatera Utara Department of Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Sumatera Utara Department of Computer Science Medan Indonesia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Universiti Putra Malaysia Faculty of Science Selangor Malaysia

ISBN: (纸本)9798331531249

In the modern era, technology has become an integral part of human life, particularly in image processing. This advanced technology is now applied to parking areas to identify vacant parking spaces. The application is designed to detect empty parking slots in real time using image data captured by a camera as input. The system then automatically scans for available parking spaces, displaying the results on the interface with green indicators for empty slots and red for occupied ones. The process involves several key stages, such as image pre-processing, image segmentation, and object detection. The system employs the Extreme Learning Machine (ELM) method to enhance its object detection capabilities. This system achieves a high accuracy rate of 94%under well-lit conditions. This novel technology optimizes parking management, providing an effective and dependable method to monitor parking availability in real time. © 2024 IEEE.

关键词： Adversarial machine learning

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Research Progress On Name Data Networking To Achieve A Superior National Product In Indonesia 8

Research Progress On Name Data Networking To Achieve A Super...

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8th International Conference on Wireless and Telematics, ICWT 2022

作者： Syambas, Nana Rachmana Juhana, Tutun Hendrawan Mulyana, Eueung Edward, Ian Joseph Matheus Situmorang, Hamonangan Mayasari, Ratna Negara, Ridha Muldina Yovita, Leanna Vidya Wibowo, Tody Ariefianto Ahdan, Syaiful Nurkahfi, Galih Nugraha Nurhayati, Ade Mulya, Hafiz Budiana, Mochamad Soebagja Institute of Technology School of Electrical Engineering and Informatics Bandung Bandung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung35132 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Faculty of Engineering Institut Teknologi Telkom Jakarta Jakarta11710 Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

Global traffic data are proliferating, including in Indonesia. The number of internet users in Indonesia reached 205 million in January 2022. This data means that 73.7% of Indonesia's population has used the internet. The median internet speed for mobile phones in Indonesia is 15.82 Mbps, while the median internet connection speed for Wi-Fi in Indonesia is 20.13 Mbps. As predicted by many, real-time traffic such as multimedia streaming dominates more than 79% of traffic on the internet network. This condition will be a severe challenge for the internet network, which is required to improve the Quality of Experience (QoE) for user mobility, such as reducing delay, data loss, and network costs. However, IP-based networks are no longer efficient at managing traffic. Named Data Network (NDN) is a promising technology for building an agile communication model that reduces delays through a distributed and adaptive name-based data delivery approach. NDN replaces the 'where' paradigm with the concept of 'what'. User requests are no longer directed to a specific IP address but to specific content. This paradigm causes responses to content requests to be served by a specific server and can also be served by the closest device to the requested data. NDN router has CS to cache the data, significantly reducing delays and improving the internet network's quality of Service (QoS). Motivated by this, in 2019, we began intensive research to achieve a national flagship product, an NDN router with different functions from ordinary IP routers. NDN routers have cache, forwarding, and routing functions that affect data security on name-based networks. Designing scalable NDN routers is a new challenge as NDN requires fast hierarchical name-based lookups, perpackage data field state updates, and large-scale forward tables. We have a research team that has conducted NDN research through simulation, emulation, and testbed approaches using virtual machines to get the best NDN router design be

关键词： Testbeds

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