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Sustainable Emerging Innovations in Engineering and Technology (ICSEIET), International Conference on

作者： Maylia Fatmawati Mega Novita Nugroho Dwi Saputro Alok Singh Chauhan Noora Qotrun Nada Amit Sinha Bambang Agus Herlambang Shruti Gupta Department of Informatics Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks.

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Modified Turnstile Antenna Design for Multi-Band and Broadband Applications

Modified Turnstile Antenna Design for Multi-Band and Broadba...

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USNC-URSI Radio science Meeting (Joint with AP-S Symposium)

作者： I-Fong Chen Chia-Mei Fei-Lung Wu Jian-An Xie Dept. of Computer and Communication School of Electronics and Electrical Engineering Dongguan Polytechnic Jinwen University of Science and Technology New Taipei City Taiwan Department of Communications Engineering Feng Chia University Taichung City Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung City Taiwan

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0.023, consists of four modified dipole elements and four power dividers, as shown in Figure. 1. For the multi-band and broadband operation, three dipole antennas were shunted to be a dipole element. The electrical-length of the dipole element can be determined from the one quarter-wave length at the 2.45 GHz, 5.5 GHz and 6.525 GHz, for covering 2400 MHz-2500 MHz, 5150 MHz-5850 MHz and 5925 MHz-7125 MHz. In order to operates in various modes such as normal mode and axial mode, the modified four port triple-band microstrip series power divider was designed. In normal mode, it radiates horizontally polarized waves where as in axial mode, it radiates circularly polarized waves. The radiated patterns of the proposed antenna as shown in Figure. 2. The features of the proposed antenna are shown in the Table I. Impressive radiated gains and efficiencies are obtained.

关键词： Power dividers Dipole antennas Conferences Glass Broadband communication Broadband antennas Microstrip

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The inquiries for efficient decarbonization in the Indonesian upstream oil and gas field

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Case Studies in Chemical and Environmental Engineering 2024年 9卷

作者： Iswara, Aditya Prana Chiang Hsieh, Lin-Han Abbas, Shahbaz Dermawan, Denny Kristianto, Sonny Department of Disaster Management Postgraduate School Universitas Airlangga Surabaya Indonesia Institute of Environmental Engineering & Management National Taipei University of Technology Taipei Taiwan Department of Engineering Management College of Electrical and Mechanical Engineering National University of Sciences and Technology Islamabad Pakistan Waste Treatment Engineering Study Program Shipbuilding Institute of Polytechnic Surabaya Jalan Teknik Kimia Kampus ITS Sukolilo Surabaya Indonesia Waste Treatment Engineering Laboratory Shipbuilding Institute of Polytechnic Surabaya Jalan Teknik Kimia Kampus ITS Sukolilo Surabaya Indonesia Department of Forensic Science Postgraduate School Universitas Airlangga Surabaya 60115 Indonesia

The mandatory carbon reduction guidelines and regulations in Indonesia encourage all industrial sectors particularly the petroleum industry to implement decarbonization programs by systematically reducing carbon emissions as part of climate action. Unlike existing carbon emission studies in the petroleum industry, the literature lacks studies on the decarbonization patterns. In addition, the effectiveness of carbon mitigation in the upstream oil and gas field has not been studied so far. This study developed short panel data models to understand the current decarbonization efforts and patterns during 2015–2018. The results indicate that the considered variables are significantly impactful in carbon reduction with different coefficient patterns. It is suggested to associate decarbonization with oil and gas production and highlight energy efficiency optimization to stabilize production. The impact of financial, human, and administrative resources on the supporting facility is critical for executing sustainable carbon mitigation projects. © 2024 The Authors

关键词： Carbon reduction Climate action Decarbonization patterns Mitigation strategies Petroleum industry

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Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority over Sampling Technique Method 1

Optimization Sentiments of Analysis from Tweets in myXLCare ...

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Saputra, Dedi Dwi Gata, Windu Wardhani, Nia Kusuma Parthama, Ketut Sakho Setiawan, Hendra Budilaksono, Sularso Yogatama, Dimas Hadiyatna, Agus Purnamasari, Endah Putri Pratama, Bryan Novianti, Deny Computer Science-Postgraduate Program Sekolah Tinggi Manajemen Informatika Dan Komputer Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science Sekolah Tinggi Manajemen Informatika Dan Komputer Nusa Mandiri Jakarta Indonesia Faculty of Computer Science-Postgraduate Program University of Mercubuana Jakarta Indonesia Graduate School Master's Degree Computer Science University Paramita Indonesia Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Bani Saleh Jakarta Indonesia Graduate School Master's Degree Computer Science University of Persada Indonesia YAI Jakarta Indonesia Graduate School Master's Degree Computer Science University of Gadjah Mada Jakarta Indonesia

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, "transform case", "tokenize", "token filters by length", "n-gram", "stemming" were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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E-THeTA: An Efficient and Adaptive TDMA Approach for Wireless Body Area Networks

E-THeTA: An Efficient and Adaptive TDMA Approach for Wireles...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Jorge F. Herculano Luís O. S. de Andrade Willians De P. Pereira Alirio S. de Sá Distributed Systems Laboratory (LaSiD) Graduate Program in Mechatronics (PPGM) Computer Science Institute (IC) and Polytechnic School (EP) Federal University of Bahia (UFBA) Ondina Campus Salvador Bahia Brazil Federal University of Southern Bahia (UFSB) Itabuna Campus Bahia Brazil Sergipe Federal Institute of Education Science and Technology (IFS) Socorro Campus Sergipe Brazil Rondônia Federal Institute of Education Science and Technology (IFRO) Porto Velho Campus Rondônia Brazil

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

Several research efforts have proposed adaptive solutions for Time Division Multiple Access (TDMA) to satisfy time requirements and save energy on Wireless Body Area Networks (WBANs) affected by radiofrequency interferences and carrying dynamic traffic. Even with the benefits, most of these solutions waste network bandwidth, decreasing network performance and the ability to recover from some unexpected communication conditions. This paper introduces the E-THeTA, an adaptive and efficient approach based on TDMA crafted to enhance performance, save energy, and efficiently use WBAN bandwidth. Compared to IEEE 802.15.4 and IEEE 802.15.6 standards and other adaptive approaches, the proposed solution reduces message losses, consumes less energy, and maintains low communication latencies in most evaluated scenarios.

关键词： Wireless communication IEEE 802.15 Standard Radio frequency Time division multiple access Adaptive systems Bandwidth Body area networks Standards

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Learning Game programming Through Narrative, Sound Effects, and Music in Text-Based Games: A Creative Approach to Digital Game Education

Learning Game Programming Through Narrative, Sound Effects, ...

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Frontiers in Education (FIE) Conference

作者： Glaucio H. M. Moro Claudio R. V. Carvilhe Bruno C. de Paula Carlos N. Silla Computer Music Technology Laboratory (Onloop) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil Polytechnic School and Digtal Games Development Course Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil Computer Music Technology Laboratory (Onloop) Graduate Program in Computer Science (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba PR Brazil

Within the tradition of Digital Game courses, the use and teaching of programming is seen as essential for students to work on skills and gain knowledge to develop an artifact. However, for students at a more basic level, this can be a major difficulty considering the complexity of understanding a language they have never seen before. Narrative, sound, and music are critical elements in game creation and can be used to teach programming concepts in a more intuitive and engaging way. Recently, the “Creative Experience: Text-Based Game Development” module has strengthened the use of narrative as a way of teaching programming concepts. Narrative creates contexts and stories for players, while sound provides feedback and guidance during the game. According to Kelleher (2006), the use of games to teach programming is an effective form of learning because it allows students to experiment with concepts in a fun and playful way and develops important skills such as problem-solving, critical thinking, and teamwork. The use of narrative, sound, and music in games can be an effective way to teach complex programming concepts clearly and engagingly. This is an article that addresses the strategies created in the digital game module “Creative Experience: Text-Based Game Development” exploring narrative, sound, and music as learning elements for programming and comparing the results within the period of two course offerings, one with only programming teaching and another with the introduction of narrative and sound as the basis for constructing this knowledge. It is hoped that these teaching strategies can contribute to the development of valuable skills in the field of education and programming.

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Architecture,Design and computer science:Professional Training for Elementary school Teachers

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Journal of Civil Engineering and Architecture 2021年第12期15卷 641-654页

作者： Andréa Quadrado Mussi Luisa Fernanda Nercolino Deon Amilton Rodrigo de Quadros Martins Postgraduate Program in Architecture and Urbanism(PPGARQ)of the Polytechnic School at Faculdade Meridional(IMED) Passo Fundo/RSCEP 99070-220Brazil Laboratory of Education Science and Innovation Faculdade Meridional(IMED)Passo Fundo/RSCEP 99070-220Brazil

Studies show that contemporary teaching methodologies are based on skills and competences directed to different areas of professional practice,such as Architecture,Design and computer *** learning spaces gain strength,stimulating the development of new skills by *** Digital Manufacturing Laboratories(Fab Labs)and Makerspaces are entering the school environment and starting a movement for change in traditional *** they are seen as potential drivers for higher quality education,the lack of specific training for teachers in the face of new digital information and communication technologies,especially in basic education,becomes limiting in the development of new active ***,this study aimed to develop a training dynamic for basic education teachers,addressing the use of tools and equipment for digital manufacturing and prototyping,2D and 3D modeling applications and software,and programming and *** methodology consists of literature review,followed by the action research method with two workshops given through an online and synchronous *** importance of incorporating creative and innovative methodologies in education is understood,and above all,there is a dynamic,planned and effective structure,geared towards the orientation of teachers.

关键词： Digital fabrication computational thinking active learning professional training

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things

Intrusion Detection using Deep Neural Network Algorithm on t...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Syariful Ikhwan Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Computer Science Informatics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia Statistics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small.

关键词： Deep learning Training Satellites Neural networks Intrusion detection Training data Feature extraction

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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