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2023 IEEE International Conference on Data and Software engineering, ICoDSE 2023

作者： Widodo, Anteng Rochim, Adian Fatchur Warsito, Budi School of Postgraduated Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Muria Kudus University Faculty of Engineering Department of Information System Kudus Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350381382

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future. copy;2023 IEEE. © 2023 IEEE.

关键词： Denial-of-service attack

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Classification of Paddy Leaf Disease Using MobileNet Model 8

Classification of Paddy Leaf Disease Using MobileNet Model

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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Masykur, Fauzan Adi, Kusworo Nurhayati, Oky Dwi Diponegoro University Doctoral Program of Information System School of Postgraduate Studies Semarang Indonesia Muhammadiyah University of Ponorogo Department of Informatics Engineering Ponorogo Indonesia

ISBN: (纸本)9781665453899

Paddy is a rice-producing plant as a staple food raw material for most of the population in Indonesia. The rice planting process cannot be separated from pests and diseases that can cause crop failure and impact on rice production stocks. Currently, the process of monitoring the development of rice plants is done conventionally by direct observation by farmers. Meanwhile, in this paper, a classification model and determination of object detection in rice plants infected with pests and diseases is proposed based on leaf color. Leaf classification applies convolution conditional network algorithm with mobilenet as the architecture. The leaf image dataset was taken from public data from *** by involving 4 classes, namely healthy leaf images, brownspot, hispa and leafblast images. In each class the leaf images are grouped into 2 groups, namely 70% training and 30% validation. On the mobilenet backbone, it produces 97% accuracy and 22% loss with the time required for 195s 19s/step in 1 epoch round. © 2022 IEEE.

关键词： Object detection

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GPU-Based Acceleration of the Rao Optimization Algorithms: Application to the Solution of Large Systems of Nonlinear Equations 24th

GPU-Based Acceleration of the Rao Optimization Algorithms:...

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24th International Conference on Intelligent Data engineering and Automated Learning, IDEAL 2023

作者： Silva, Bruno Lopes, Luiz Guerreiro Doctoral Program in Informatics Engineering University of Madeira Madeira Is. Funchal Portugal Regional Secretariat for Education Science and Technology Regional Government of Madeira Funchal Portugal Faculty of Exact Sciences and Engineering University of Madeira Madeira Is. Funchal9020-105 Portugal

ISBN: (纸本)9783031482311

In this paper, parallel GPU-based versions of the three Rao metaphor-less optimization algorithms are proposed and used to solve large-scale nonlinear equation systems, which are hard to solve with traditional numerical methods, particularly as the size of the systems get bigger. The parallel implementations of the Rao algorithms were performed in Julia and tested on a high-performance GeForce RTX 3090 GPU with 10 496 CUDA cores and 24 GB of GDDR6X VRAM using a set of challenging scalable systems of nonlinear equations. The computational experiments carried out demonstrated the efficiency of the proposed GPU-accelerated versions of the Rao optimization algorithms, with average speedups ranging from 57.10 × to 315.12 × for the set of test problems considered in this study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graphics processing unit

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks 4

A Novel Blockchain-Based Strategy for Energy Conservation in...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Sharma, Khemraj Goyal, Rohit Bhagat, Sukesh Kumar Agarwal, Sheenu Bisht, Girish Singh Hussien, Mohemmed Nanhua University Department of Business Administration Doctoral Program in Management Sciences Dalin62248 Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Uttaranchal University Department of Computer Science & Engineering Dehradun India Uttarakhand Technical University Department of Electronics and Communication Engineering Dehradun India Tula's Institute Dehradun India Department of Computer Science and Engineering Dehradun248001 India The Islamic University Najaf Iraq

ISBN: (纸本)9798331528324

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency. © 2024 IEEE.

关键词： Blockchain Network Network Communication security Wireless Sensor

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Influence of Project and Affected Local Community Interests Level on Social Conflicts in Indonesian Infrastructure Projects

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International Journal of engineering, Transactions A: Basics 2022年第7期35卷 1217-1226页

作者： Sanggoro, H.B. Alisjahbana, S.W. Mohamad, D. Civil Engineering Doctoral Program University of Tarumanagara Jakarta Indonesia

Poor economic growth due to Covid-19 pandemic in the last two years has resulted in a decline in indicators of public welfare. The construction industry sector also experienced a severe decline in productivity;thus, adjustments had to be made to survive the crisis situation. In addition, environmental problems due to development activities also threaten the lives and incomes of people who depend on natural products. These conditions encourage the escalation of interests that affect infrastructure projects in Indonesia. This study aimed to predict the influence of project and affected local community's interests on infrastructure projects social conflicts. Data were obtained by questionnaire from 68 project managers as respondents and analyzed using PLS-SEM. The findings of this study are that the influence of affected community is more dominant than that of project interests on project social conflicts. This shows the important role of communities in the concept of sustainable development with environmental and social perspectives. The results of this study will be useful in drafting the concept of an integrated and standardized environmental and social safeguard framework. To achieve an appropriate framework, further research is needed to examine the framework concept as a moderation of the relationship between interests and project social conflicts. © 2022 Materials and Energy Research Center. All rights reserved.

关键词： Construction industry

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The effect of Zn2+ ion substitution on nanoparticles of ZnxMn(1-X) Fe2O4 (X = 0.2, 0.4, 0.6, 0.8) towards signal surface plasmon resonance (SPR)

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Materials Research Innovations 2023年第6期27卷 420-428页

作者： Almi Putra, Rachmad Andi Fadlly, Teuku Asmara, Vina Monica, Desy Sartika Hijrahnisa Novia, Wina Yakob, Muhammad Indrayana, Ipt Geubrina Raszky, Rachmita Mujahid Hamdan, Abd Arfi, Febrina Rahwanto, Adi Jalil, Zulkarnain Physics Study Program Faculty of Engineering Universitas Samudra Langsa Indonesia Doctoral Study Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Physics Education Faculty of Teaching and Education Universitas Samudra Langsa Indonesia Physics Study Program Faculty of Natural Science and Engineering Technology Universitas Udayana Denpasar Indonesia Department of Environmental Engineering Faculty of Science and Technology UIN Ar-Raniry Banda Aceh Indonesia Department of Chemistry Faculty of Science and Technology UIN Ar-Raniry Banda Aceh Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Syiah Kuala University Banda Aceh Indonesia

This study examined the effect of Zn2+ ion substitution on ZnxMn(1-x) Fe2O4 nanoparticles (x = 0.2, 0.4, 0.6, 0.8) towards signal Surface Plasmon Resonance (SPR). The co-precipitation method synthesised the nanoparticles at a temperature of 90°C. The X-Ray Diffraction (XRD) results showed a polycrystalline structure with a crystallite size varied from 19.05 nm to 19.77 nm. In addition, as the Zn element increased in the ZnxMn(1-x)Fe2O4 nanoparticles, the degree of crystallinity increased by 21.72%, respectively. The optical properties of the nanoparticles ZnxMn(1-x) Fe2O4 indicate absorbance at wavelengths of 330.36–331.74 nm. The absorbance level increased as the Zn element decreased, resulting a direct optical gap of 3.47 eV and an indirect optical gap of 3.23 eV. The Attenuated Total Reflection (ATR) curve in the SPR biosensor indicated a shift in the reflective angle of the Prism/Au/Air configuration, which is 47.78° on Prisma/Au/nanoparticle of Zn0.8Mn0.2Fe2O4/air by 50.75°, and the prism/Au/nanoparticle of Zn0.2Mn0.8Fe2 O4/air by 51.04°. The substitution of Zn ions in the compound Zn x Mn(1-x)Fe2O4 affects the resulting ATR curve. The shift in the SPR angle is caused by changes in the refractive index in each layer which is influenced by the concentration of each material which has an impact on the absorbance ability and photon reflectance. This research has the potential to continue with the testing of dielectric and magnetic properties, as well as special testing for biological analytes compatible with ZnxMn(1-x)Fe2O4 nanoparticles. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Surface plasmon resonance

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Application of Digital Gamification Systems in Intelligent Automated Learning 10th

Application of Digital Gamification Systems in Intelligent A...

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10th International Conference on Kansei engineering and Emotion Research, KEER 2024

作者： Lee, Chein-Hui Saputri, Evelyn Chiu, Min-Chi Department of Information Technology Ling Tung University Taichung Taiwan Doctoral Program of Intelligent Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Intelligent Manufacturing Technology Ling Tung University Taichung Taiwan Department of Multimedia Design National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819798896

This study aims to develop an innovative digital game-based teaching platform designed to facilitate students’ learning of intelligent automation-related knowledge through immersive game scenarios. This method offers significant advantages, including the provision of an engaging and challenging learning environment that encourages active student participation while fostering critical thinking and problem-solving skills. Students can apply the knowledge they acquire in practical game contexts, thereby enhancing their learning outcomes, aligning with the contemporary educational demand for diversified teaching approaches. Furthermore, this pedagogical strategy effectively stimulates students’ motivation to learn by engaging them in problem-solving and task completion activities, consequently improving their learning efficiency. More importantly, it emphasizes not just the transmission of knowledge but also the cultivation of students’ cognitive abilities and problem-solving skills, which are crucial for their future academic and professional development. By integrating educational content with interactive game elements, the platform can adapt to various learning paces and styles, making education more personalized and accessible. Overall, this study aims to provide students with a highly engaging and efficient learning method that meets the modern educational requirements for innovative teaching models, thereby significantly improving students’ learning outcomes and addressing their educational needs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Meshless generalized finite difference method with a domain-selection method for solving degenerate boundary problems

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engineering Analysis with Boundary Elements 2023年第1期152卷 185-193页

作者： Huang, Ji Lyu, Hong-Guan Fan, Chia-Ming Chen, Jiahn-Horng Naval Architecture and Shipping College Guangdong Ocean University Zhanjiang524088 China School of Ocean Engineering and Technology Sun Yat-sen University Zhuhai519082 China Doctoral Degree Program in Ocean Engineering Technology National Taiwan Ocean University Keelung20224 Taiwan Department of Harbor and River Engineering National Taiwan Ocean University Keelung20224 Taiwan Shih Yen-Ping Center for Underwater Technology National Taiwan Ocean University Keelung20224 Taiwan National Academy of Marine Research Kaohsiung80661 Taiwan

This paper is dedicated to presenting a combined Generalized Finite Difference Method (GFDM) and Domain-Selection Method (DSM) approach for solving degenerate boundary problems which are usually encountered in engineering simulations. Compared with traditional Domain-Decomposition Method (DDM), the most critical advantage of the present GFDM-DSM is that it is entirely free from matching continuous boundary conditions for different parts of the fluid domain of interest, which is sometimes hard to perform for specific situations. Instead, the only prerequisite of the present GFDM-DSM is to identify which sub-domain each computational node belongs to, and then a linear algebraic system can be assembled according to the pattern of domain-selection. Four benchmarks are performed to validate the convergence and accuracy of the present GFDM-DSM. It is shown that the present GFDM-DSM holds satisfactory accuracy for solving degenerate boundary problems. In particular, the present DSM is a flexible strategy since it can be also combined with other localized domain-type meshless methods in practical applications. © 2023 Elsevier Ltd

关键词： Domain decomposition methods

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification 13

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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13th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2023

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798350347319

Sports Science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport Science recognizes and prevents injuries. Sensors and statistics formalize Sports Science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM2.5 and PM10). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM2.5 and PM10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results. © 2023 IEEE.

关键词： Classification (of information)

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