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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Risitiani, Nadia Saida, Nur Fauzi, Rahmat Noor Darmawan, Helmy Yusuf Sanubari, Agung Oktivasari, Prihatin Wardhani, Rika Novita Riandini Politeknik Negeri Jakarta Department of Electrical Engineering Depok Indonesia Politeknik Negeri Jakarta Department of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798350399615

Recording the results of checking blood sugar, cholesterol and uric acid has not made a routine history every time you check, this is what makes us make an application in the Non-Invasive Home Medical Check-up tool, Non-Invasive Home Medical Check-up is a medical device made using non-invasive methods in checking blood sugar, uric acid, cholesterol, and body temperature. This tool uses MAX30105 sensors to check uric acid, blood glucose, and cholesterol levels, and MLX90614 to check body temperature. This application is connected to the device in real-time and there is a history check display to make it easier for users to review previous checks. Making applications using MIT APP INVENTOR and using the PostgreSQL database. To ensure that everything is as expected, testing is carried out to send and store data on the results of checking the device to the database and displayed in the application. Data transmission testing is done by determining the delay time of sending tools to the database using the internet network, and the average delay in the experiment is 0.0206 seconds. Integration testing is done by comparing the data in the database with the application by means of the user must enter the correct and appropriate ID. © 2022 IEEE.

关键词： MAX30105 Sensor Medical Check-up Application MLX90614 Sensor

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WSports-50: An Image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Experimental Learning in Electrical engineering with the Erasmus+ DIG-SENSING Blended Intensive Program 32

Experimental Learning in Electrical Engineering with the Era...

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32nd Annual Conference of the European Association for Education in Electrical and Information engineering, EAEEIE 2023

作者： Laura, Grindei Lorente, Vicent Posadas-Yague, Juan-Luis Grout, Ian Fischer, Thomas Technical University of Cluj-Napoca Department of Electrotechnics and Measurements Cluj-Napoca Romania Universitat Politècnica de València School of Informatics Department of Computer Engineering Valencia Spain University of Limerick Department of Electronic and Computer Engineering Limerick Ireland University of Applied Sciences Campus Vienna Department Engineering Vienna Austria

ISBN: (纸本)9789038656908

This paper presents the Erasmus+ DIG-SENSING project which is a Blended Intensive Program (BIP) that has the main objective to increase the motivation of students in achieving knowledge and practical experience in the field of electronic circuits. This is to be achieved through projects that integrate the design of electronic circuits, and the programming of microcontrollers through an initial online series of Webinars and local activities within the partner institutions from March to May 2023 that are then followed by a physical Summer School in July 2023. The structure and implementation of DIG-SENSING, with the benefits for students, academic staff, and partner universities will be presented. In this project, the participating students must develop and tackle a challenge that brings together Information and Communication Technologies in the framework of solving socio-environmental *** paper also presents and evaluation of students perception after the first webinar. © 2023 Fontys University of Applied Science.

关键词： Students

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Genera: A Serious Game Leveraging Blockchain Technology for Renewable Energy Education 14

Genera: A Serious Game Leveraging Blockchain Technology for ...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Stamatakis, Dimitris Kogias, Dimitrios G. Leligou, Helen C. Karkazis, Panagiotis A. Penalvo, Elisa Department of Industrial Design and Production Engineering University of West Attica Petrou Ralli Athens12243 Greece Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Athens12210 Greece Institute of Energy Engineering U. Politecnica de Valencia Spain

ISBN: (纸本)9781837242689

The GENERA Card Game combines serious gaming and blockchain technology to raise awareness on renewable energy. As part of the GENERA project, this multiplayer game engages players by assigning them the role of a town's mayor tasked with expanding their town using sustainable energy sources. The game integrates educational content with interactive gameplay, utilizing NFTs for the in-game assets and ERC-20 tokens as rewards. Developed using web technologies, the game benefits from extensive accessibility while maintaining high-quality features. A survey of 45 players indicated high satisfaction with the game's educational impact and accessibility, though it highlighted areas for improvement in user experience and mechanics. The study underscores the potential of serious games in promoting environmental awareness and the benefits of blockchain technology in enhancing educational tools. Future updates will focus on adding new features, improving user interface and experience, and integrating realistic data on renewable energy sources to further enrich the educational content. The findings suggest that serious games like GENERA can effectively raise awareness and foster a deeper understanding of renewable energy, thereby supporting global sustainability efforts. © The Institution of engineering & Technology 2024.

关键词： Clean energy

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Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

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computers, Materials & Continua 2022年第1期70卷 1875-1891页

作者： Talha Imran Muhammad Attique Khan Muhammad Sharif Usman Tariq Yu-Dong Zhang Yunyoung Nam Yunja Nam Byeong-Gwon Kang Department of Computer Science COMSATS University IslamabadWah CampusWah CanttPakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Informatics University of LeicesterLeicesterUK Department of ICT Convergence Soonchunhyang UniversityAsanKorea

Malaria is a critical health condition that affects both sultry and frigid region worldwide,giving rise to millions of cases of disease and thousands of deaths over the *** is caused by parasites that enter the human red blood cells,grow there,and damage them over ***,it is diagnosed by a detailed examination of blood cells under the *** is the most extensively used malaria diagnosis technique,but it yields limited and unreliable results due to the manual human *** this work,an automated malaria blood smear classification model is proposed,which takes images of both infected and healthy cells and preprocesses themin the L^(*)a^(*)b^(*)color space by employing several contrast enhancement *** extraction is performed using two pretrained deep convolutional neural networks,DarkNet-53 and *** features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization *** classifiers are effectuated on the reduced features,and the achieved results excel in both accuracy and time compared to previously proposed methods.

关键词： Malaria preprocessing deep learning features optimization classification

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Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making 6

Improving Communication Systems Through Federated Learning: ...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Stergiou, Konstantinos D. Ishibashi, Yutaka Goudos, Sotirios K. Lagkas, Thomas Sarigiannidis, Panagiotis Psannis, Konstantinos E. University of Macedonia Department of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Aristotle University of Thessaloniki Department of Physics Thessaloniki Greece International Hellenic University Department of Computer Science Kavala Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece

ISBN: (纸本)9798350339505

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems. © 2023 IEEE.

关键词： Learning systems

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Integration of Machine Learning Tools with Lean Six Sigma Activities in Forensic engineering: A Literature Review 6

Integration of Machine Learning Tools with Lean Six Sigma Ac...

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6th International Conference on Quality engineering and Management, ICQEM 2024

作者： Alkindi, S.A. Araci, Z.C. Alqassim, M.A. Bouridane, A. Department of Industrial Engineering and Engineering University of Sharjah Sharjah27272 United Arab Emirates Specialty Forensic Evidence Department General Department of Forensic Science and Criminology Dubai Police GHQ Dubai1493 United Arab Emirates Department of Computer Engineering Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

ISBN: (纸本)9789895491124

Purpose – This paper addresses challenges in forensic engineering, particularly in the detection and analysis of material failures. It explores the integration of Machine Learning (ML) techniques, specifically Convolutional Neural Networks (CNN), within the Lean Six Sigma (LSS) DMAIC methodology to streamline and automate the process of material failure detection. It aims to contribute towards leaner, more efficient, and cost-effective methods for forensic engineering investigations. Methodology - The methodology employed in this study entailed a thorough literature search, involving the selection of relevant articles based on predefined criteria, their subsequent categorization into distinct sections, a careful analysis of key insights, and the identification of significant research gaps. Utilizing the VOSviewer software®, bibliometric analysis was conducted to examine the collected data. This paper offers a comprehensive review of the existing literature, conducts a rigorous analysis, outlines research gaps, and proposes future avenues for exploration in this interdisciplinary domain. Findings – The paper identified various difficulties within forensic engineering, including challenges such as expert time loss and complex processes, alongside emphasizing ML’s promise in detecting material failures and proving for its combination with LSS to enhance operational efficiency. Practical implications- Forensic engineers can leverage the identified synergy to enhance efficiency in their practices, while lean experts may optimize processes by integrating ML technology for automation and process improvement. Originality/value - The originality lies in the interdisciplinary fusion of forensic engineering, ML, and LSS, offering fresh insights to improve material failure detection processes. © 2024 Universidade do Minho. All rights reserved.

关键词： Convolution

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Comparative Study of Machine Learning and Holt-Winters Exponential Smoothing Models for Prediction of CPI's Seasonal Data 2

Comparative Study of Machine Learning and Holt-Winters Expon...

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2nd International Conference on Software engineering and Information Technology, ICoSEIT 2024

作者： Afrah, Ashri Shabrina Sari, Nur Fitriyah Ayu Tunjung Utama, Shoffin Nahwa Holle, Khadijah Fahmi Hayati Lestandy, Merinda Sintiya, Endah Septa Rizdania Universitas Islam Negeri Maulana Malik Ibrahim Malang Department of Informatics Engineering Malang Indonesia Universitas Muhammadiyah Malang Department of Electrical Engineering Malang Indonesia State Polytechnic of Malang Information Technology Department Malang Indonesia University of Pgri Wiranegara Computer Science Department Pasuruan Indonesia

ISBN: (纸本)9798350317503

Inflation is one of the factors influencing price stability. Inflation affects people's purchasing power and impacts their decisions as economic actors. Consumer Price Index (CPI) is one of the factors used by economists to measure the inflation or deflation in a country. This research focuses on comparing the prediction results of the CPI for the Education Goods and Services using the Holt's Winters Exponential Smoothing and Machine Learning Methods, namely Long Short-Term Memory (LSTM), Extreme Learning Machine (ELM), Ridge, and Least Absolute Shrinkage and Selection Operator (LASSO). The data used is univariate data on the CPI for the Education Goods and Services in Malang City in 1996-2013, which was obtained from the publication of the Statistics Indonesia (BPS), entitled 'Malang City in Figures' which was published in 1997-2014. The results of this research show that the Ridge Method produces the smallest Mean Absolute Percentage Error (MAPE) value compared to other Machine Learning Methods and the Multiplicative Holt-Winters Exponential Smoothing Method, with MAPE= 2.10723%%. Machine Learning models that have been simulated have very good accuracy values, with MAPE values © 2024 IEEE.

关键词： Forecasting

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider 6

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Atmaja, Ardian Budi Kusuma Hidayat, Egi Muhammad Idris Riyanto Trilaksono, Bambang Siregar, Simon Habibullah, Natsir School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Telkom University Doctoral Program of Electrical Engineering Department Computer Engineering Faculty of Applied Science Indonesia

ISBN: (纸本)9798350358346

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees. © 2023 IEEE.

关键词： Geometry

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Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

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engineering 2024年第8期39卷 74-83页

作者： Usman L.Abbas Yuxuan Zhang Joseph Tapia Selim Md Jin Chen Jian Shi Qing Shao Department of Chemical and Materials Engineering University of KentuckyLexingtonKY 40506USA Department of Biosystems and Agricultural Engineering University of KentuckyLexingtonKY 40506USA Institute for Biomedical Informatics Department of Computer ScienceUniversity of KentuckyLexingtonKY 40506USA

Non-ionic deep eutectic solvents(DESs)are non-ionic designer solvents with various applications in catalysis,extraction,carbon capture,and ***,discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES *** search for DES relies heavily on intuition or trial-and-error processes,leading to low success rates or missed *** that hydrogen bonds(HBs)play a central role in DES formation,we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning(ML)models to discover new DES *** first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics(MD)simulation *** analysis reveals that DES systems have two unique features compared to non-DES systems:The DESs have①more imbalance between the numbers of the two intra-component HBs and②more and stronger inter-component *** on these results,we develop 30 ML models using ten algorithms and three types of HB-based *** model performance is first benchmarked using the average and minimal receiver operating characteristic(ROC)-area under the curve(AUC)*** also analyze the importance of individual features in the models,and the results are consistent with the simulation-based statistical ***,we validate the models using the experimental data of 34 *** extra trees forest model outperforms the other models in the validation,with an ROC-AUC of *** work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

关键词： Machine learning Deep eutectic solvents Molecular dynamics simulations Hydrogen bond Molecular design

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