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International Conference on Advanced Communication Technology (ICACT)

作者： Lan Dong Thi Ngoc Ngo-Thi-Thu-Trang Kyoung-Mok Kim Vung Pham Ha-Nam Nguyen Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi CO Vietnam Department of Medical Information Sahmyook Health University Seoul Republic of Korea Computer Science Department Sam Houston State University Huntsville Texas TX USA Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R 2 metrics to evaluate the accuracy of the *** experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful *** study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes.

关键词： Support vector machines Economic indicators Biological system modeling Predictive models Macroeconomics Forecasting Unemployment Random forests Optimization Long short term memory

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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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Classification of chicken egg quality using faster region convolutional neural network

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AIP Conference Proceedings 2024年第1期2987卷

作者： Amira Nurul Amanda Ivan Jaya Fanindia Purnamasari Research Laboratory in Data Science Intelligent System and Software Engineering Faculty of Computer and Information Technology Universitas Sumatera Utara Medan Indonesia

Chicken eggs are one of animal product which have high nutrition with cheaper price compared to another animal product. As one of the most consumed food, the quality of chicken eggs need to be more considered. According to SNI 3926:2008 and Egg Grading Manual Booklet, the quality of chicken egg can be seen from inside and also external characteristic of egg (egg shell). The internal characteristic are the yolk, egg whites, blood stains. The external characteristic are color, texture, cleanliness, thickness of the shell. This study builds a system which can detect the quality of chicken eggs by seeing the texture and the color of the shell. The quality of that chicken eggs grouped into three levels which is Grade A, Grade B, and Grade C. The amount of data that used in this study are 60 data that divided into 480 data for training and 120 data for testing. Data were doing pre-processing before classified with Faster Region Convolutional Neural Network method with ResNet-50 as pre-trained model. Based on the result of training system, this system can classify the quality of chicken eggs with an accuracy rate of 94%. The performance of the system in classifying egg quality is very sufficient.

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Analysing the Impact of COVID-19 outbreak and Economic Policy Uncertainty on Stock Markets in Major Affected Economies 6

Analysing the Impact of COVID-19 outbreak and Economic Polic...

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6th Smart Cities Symposium, SCS 2022

作者： Matuka, Adelajda Asafo, Shuffield Seyram Eweke, Gamaliel O. Mishra, Pradeep Ray, Soumik Abotaleb, Mostafa Makarovskikh, Tatiana Alhussan, Amel Ali Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Dutta, P.K. Chowdhury, Subrata University of Macerata Department of Economics and Law Italy Niger Delta University Department of Banking & Finance Nigeria J.N.K.V.V. College of Agriculture M.P. Powarkheda India Centurion University of Technology and Management Odisha Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog Egypt School of Engineering and Technology Amity University West Bengal Kolkata India AP Chittoor India

ISBN: (纸本)9781839538544

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Investments

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Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference system (ANFIS) Method 5

Plant Seeds Growth Prediction on Greenhouse Using Adaptive N...

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5th International Conference on Energy, Environmental and Information system, ICENIS 2020

作者： Ayu Widiana, Siska Suryono, Suryono Warsito, Budi Master of Information System Post Graduated School Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

Food security is a problem that every country had, especially for poor and developing countries. To improve the food security one of the solutions that can be applied is to collaborate technology and agriculture such as greenhouse. The technology that is applied to greenhouse can produce plants with good quality. Good quality plant can be predicted with prediction on the plant seeds in order to develop the plants production just as we expected. Prediction on plant seeds is using the adaptive neuro fuzzy inference system (ANFIS) model which is a combination of fuzzy and neural network. ANFIS will process the data with high complexity and it will provide the prediction result with high accuracy. Plant seeds prediction is using 65 data which divided into two data, specifically 50 training data and 15 testing data. The prediction provides accurate result and will generate 14/15 x 100% = 93.3333% precision with Mean Absolute Deviation (MAD) is 64.3391 from 15 prediction data about 4.2893, Mean Absolute Percentage Error (MAPE) is 5.3485 from 15 prediction data about 0.35657, Mean Square Deviation (MSD) is 9.159 from 15 prediction data about 0.6106. © The Authors, published by EDP sciences, 2020.

关键词： Forecasting

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An intelligent guided-airborne virus detection

An intelligent guided-airborne virus detection

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International Conference on sciences and Techniques of Automatic Control and computer engineering (STA)

作者： Ahmed Hadded Mossaad Ben Ayed Shaya A. Alshaya Computer and Embedded System Laboratory National Engineering School Of Sfax Sfax University Sfax Tunisia Electronic Industrial ENISoSousse University Tunisia Computer and Embedded System Laboratory ENIS Sfax University Tunisia Sfax Tunisia Department of computer science Faculty of sciences and humanities sciences Majmaah University Dean of digital transformation and e-transactions Prince Sultan University Majmaah Saudi Arabia

ISBN: (纸本)9781665482622

Health has recently faced many challenges, including improving a healthy environment and reducing human life's dangers and economic crises. The last pandemic COVID-19 had badly affected survivor sectors with infection and lockdown exigence. Scientists proposed several solutions to reduce the negative impact of a such pandemic by proposing systems for earlier detection of viruses. The use of metamaterials as an emerging technology in the biosensors field allows a high accuracy. This paper presents a method for detecting and capturing airborne viruses using metasurface technology. The goal is to develop a system that can identify and capture these viruses using FET sensors. The accuracy of the detection is tied to the concentration of aerosols. The model proposes a guided flow of aerosols that positively impacts the detection of viruses through the FET biosensor. The simulation results based on Concentration and airflow velocity delays prove the proposed model's performance.

关键词： COVID-19 computer viruses Pandemics Atmospheric modeling Biological system modeling Field effect transistors Aerosols

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Multi-Criteria Decision Making with the VIKOR and SMARTER Methods for Optimal Seller Selection from Several E-Marketplaces 5

Multi-Criteria Decision Making with the VIKOR and SMARTER Me...

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5th International Conference on Energy, Environmental and Information system, ICENIS 2020

作者： Arif, Miftahul Suseno, Jatmiko Endro Isnanto, R. Rizal Master of Information System Post Graduated School Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

Many similar products offered by several sellers in e-marketplace applications make the buyers need to be more selective when shopping. This article addresses the prioritization of the goods sellers alternatives in e-marketplace by integrating the VIKOR with the SMARTER method for the process of multi-criteria decision making (MCDM) by considering various variables as selection criteria. There are five variables of sellers in the e-marketplace that are used as selection criteria, namely: product price, number of products sold, seller score rating, number of reviews with five stars, and location distance. The SMARTER method is used to determine the criteria weight, and the VIKOR model to rank the alternative priorities according to the weighted index. The case studies are conducted on three e-marketplace in Indonesia with the most number of monthly web visitors, namely: Shopee, Tokopedia, and Bukalapak. Finally, integration of SMARTER and VIKOR method allows the buyers to get the good products at competitive prices from the optimal goods sellers in the e-marketplace. © The Authors, published by EDP sciences, 2020.

关键词： Decision making

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FetalNet: Low-light fetal echocardiography enhancement and dense convolutional network classifier for improving heart defect prediction

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Informatics in Medicine Unlocked 2022年 35卷

作者： Sutarno, Sutarno Nurmaini, Siti Partan, Radiyati Umi Sapitri, Ade Iriani Tutuko, Bambang Naufal Rachmatullah, Muhammad Darmawahyuni, Annisa Firdaus, Firdaus Bernolian, Nuswil Sulistiyo, Deny Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Indonesia Faculty of Medicine Universitas Sriwijaya Indonesia Division of Fetomaternal Department of Obstetrics and Gynaecology Mohammad Hoesin General Hospital Indonesia

Background: Fetal heart defect (FHD) examination by ultrasound (US) is challenging because it involves low light, contrast, and brightness. Inadequate US images of fetal echocardiography play an important role in the failure to detect FHDs manually. The automatic interpretation of fetal echocardiography was proposed in a previous study. However, the low quality of US images reduces the prediction rate of computer-assisted diagnosis results. Methods: To increase the FHD prediction rate, we propose low-light fetal echocardiography enhancement stacking with a dense convolutional network classifier named “FetalNet.” Our proposed FetalNet model was developed using 460 US images to produce an image enhancement model. The results showed that all raw US images could be improved with satisfactory performance in terms of increasing the peak signal-to-noise ratio of 30.85 dB, a structural similarity index of 0.96, and a mean squared error of 18.16. Furthermore, all reconstructed images were used as inputs in a convolutional neural network to generate the best classifier for predicting FHD. Results: The proposed FetalNet model increased the FHD prediction rate by approximately 25% in terms of accuracy, sensitivity, and specificity and produced 100% predictive negative using unseen data. Conclusions: The proposed deep learning model has the potential to identify FHD accurately and shows potential for practical use in identifying congenital heart diseases in the future. © 2022 The Author(s)

关键词： Congenital heart disease Deep learning Low light image enhancement Ultrasound

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Dry etch processing in fan-out panel-level packaging - An application for high-density vertical interconnects and beyond

Dry etch processing in fan-out panel-level packaging - An ap...

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Electronic Components and Technology Conference (ECTC)

作者： Friedrich-Leonhard Schein Christian Voigt Lutz Gerhold Ioannis Tsigaras Mohamed Elghazzali Hirofumi Sawamoto Ewald Strolz Roland Rettenmeier Ruben Kahle Lars Bö ttcher Faculty IV &#x2013 Electrical Engineering and Computer Science Technische Universit&#x00E4 t Berlin Berlin Germany Business Unit Advanced Packaging Evatec AG Tr&#x00FC bbach Switzerland System Integration &amp Interconnection Technologies Fraunhofer IZM Berlin Germany

In this work, we demonstrate advances in plasma dry etch technology applied to common materials in semi-additive fineline processing for panel-level packaging. Emphasis is placed on deep reactive-ion etching into organic dielectric build-up materials as a scalable process for making vertical interconnects (vias). A capacitively coupled dual frequency plasma module was used for our experiments. We investigate the influence of different process parameters and gas mixtures on via shape, via sidewall quality and residue formation. To enable material-selective etching, a metallic hard mask is used, patterned by direct imaging lithography. For the removal of the hard mask we introduce a non-etching lift-off approach. Furthermore, a dry etch removal of sputtered Ti barrier and Cu seed layers is shown.

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Protocol for the fabrication of self-standing (nano)cellulose-based 3D scaffolds for tissue engineering

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STAR Protocols 2025年第1期6卷 103583页

作者： Mohan, Tamilselvan Bračič, Matej Bračič, Doris Lackner, Florian Nagaraj, Chandran Štiglic, Andreja Dobaj Kargl, Rupert Kleinschek, Karin Stana Graz University of Technology Institute for Chemistry and Technology of Biobased System (IBioSys) Stremayrgasse 9 Graz 8010 Austria University of Maribor Faculty of Mechanical Engineering Laboratory for Characterization and Processing of Polymers Smetanova Ulica 17 Maribor 2000 Slovenia Department of Internal Medicine Division of Pulmonology Medical University of Graz Graz Austria Institute of Automation Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Members of the European Polysaccharide Network of Excellence (EPNOE) Austria

Three-dimensional (3D) and porous scaffolds made from nanocellulosic materials hold significant potential in tissue engineering (TE). Here, we present a protocol for fabricating self-standing (nano)cellulose-based 3D scaffolds designed for in vitro testing of cells from skin and cartilage tissues. We describe steps for preparation of nanocellulose ink, scaffold formation using 3D printing, and freeze-drying. We then detail post-processing procedures to enhance mechanical properties, stability, and biocompatibility. This protocol offers researchers a framework for developing versatile and sustainable biomaterials for regenerative medicine. For complete details on the use and execution of this protocol, please refer to Mohan et al.¹ and Štiglic et al.² © 2025 The Authors

关键词： Biotechnology and bioengineering Chemistry Material sciences Tissue engineering

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