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2023 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Bousbaa, Zineb Bencharef, Omar Cady Ayyad University Computer and Systems Engineering Laboratory Faculty of Sciences and Technology Computer Science Department Marrakesh Morocco

ISBN: (纸本)9798350322972

Machine learning has been widely used as part of financial markets investment strategies, whether for forecasting the financial assets exchange rate, managing market volatility, or solving different classification problems that help with decision-making. Building an investment strategy using a scientific approach requires a massive amount of data, good computational power, and some expertise in the finance industry. Machine learning applications to the financial field, such as price exchange rate prediction, market pattern recognition, or other trading strategy tasks, are considered optimization problems. As they require an efficient algorithm dedicated to finding a global optimum, they can be solved using metaheuristics. In this survey, we study how metaheuristic optimization techniques contribute to building a robust learning model dedicated to financial investment strategy applications. © 2023 IEEE.

关键词： Financial markets

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Dramatizing Software Patterns: Focus Group Report 28

Dramatizing Software Patterns: Focus Group Report

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Vranic, Valentino Vranic, Aleksandra Vranic, Branislava Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9798400700408

The focus group on drama patterns we organized at EuroPLoP 2023 attracted 16 participants (apart from the organizers), who together built a short drama play (created by Aleksandra Vranic) based on the motifs from the stories of Alice in Wonderland. We discussed how particular drama patterns metaphorically correspond to some software patterns and elaborated on two examples of such correspondence: the Thoughts Reflecting Environment drama pattern and its correspondence to the Observer design pattern and the Loosely Coupled Situations drama pattern and its correspondence to the Visitor design pattern. Both drama patterns were first played by the participants and then related to the corresponding design pattern. We also played Observer in a technical, nonmetaphoric way. We also made a performance at the banquet, which was accepted with delight. We achieved this with no rehearsals and with the actor for the main role appointed just before the performance, which confirmed once more how powerful patterns are. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： design patterns drama patterns organizational patterns pattern composition

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A Scientometric Analysis of electric vehicle-based text mining 7

A Scientometric Analysis of electric vehicle-based text mini...

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7th Information systems International Conference, ISICO 2023

作者： Puspita, Angella Natalia Ghea Surjandari, Isti Kemala, Bunga Kharissa Laras Industrial Engineering Department Faculty of Engineering Universitas Indonesia Depok Indonesia Center of Research Mining Technology National Research and Innovation Agency PUSPIPTEK Serpong Indonesia Information and Decision Systems Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia

The United Nations Climate Change Conference (COP 21) held in Paris on December 12, 2015, agreed on the Paris Agreement, which aims to address climate change and its negative impacts as part of the challenge of Global Warming. The transportation sector contributed 23% of CO2 emissions of a global contributor in 2018. Electric vehicles themselves have benefited from an environmental and economic perspective. The development of electric vehicles in Indonesia has increased the number of electric vehicle users as well as research related to customer awareness, opinions, preferences, and satisfaction, identification of markets and potential for technology and innovation, predictions, new policies, literature reviews and topics, keywords and fields related to electric vehicle. From the results of this study, it is expected to be able to find out customer characteristics, customer concerns, market penetration, model social impact data, predict market growth and technology developments, and formulate policies and regulations according to the needs of stakeholders Therefore, a systematic review is needed regarding the publication of electric vehicles based on text mining, then analyzes scientometric analysis Bibliographic database files from 2011 - 2023. © 2023 The Authors. Published by Elsevier B.V.

关键词： Electric vehicles

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Surface adhesion enhancement of anodized A5052 aluminum alloy with low-power atmospheric pressure plasma jets in order to recover wettability

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Keikinzoku/Journal of Japan Institute of Light Metals 2024年第8期74卷 352-357页

作者： Kuwano, Ryoichi Hino, Makoto Tokunaga, Tsuyoshi Ebisawa, Mizue Koga, Toshihiko Hagen, Nathan Nagata, Norihito Yamashita, Michiru Department of Mechanical Systems Engineering Faculty of Engineering Hiroshima Institute of Technology 2-1-1 Miyake Saeki-ku Hirosima-shi Hiroshima731-5193 Japan Department of Innovative Mechanical and Electronic Engineering Faculty of Engineering Chiba Institute of Technology Chiba Narashino-shi Japan Tokyo Metropolitan Industrial Technology Research Institute Koto-ku Tokyo Japan Department of Mechanical Engineering Undergraduate course in Bachelor of Science in Manufacturing Technology Polytechnic University Kodaira-shi Tokyo Japan Center for Optical Research and Education （CORE） Utsunomiya University Tochigi Utsunomiya Japan SURTECH NAGATA Co. Ltd. Hyogo Akashi Japan Hyogo Prefectural Institute of Technology Hyogo Kobe-shi Japan

We focused a low-power atmospheric pressure plasma jet (APPJ) on a specimen of anodized A5052 aluminum alloy with degraded surface wettability due to atmospheric exposure for more than 30 days. We show that an APPJ driven at about 10 W of power can restore surface wettability. To demonstrate this, specimens were irradiated with an APPJ using He as the carrier gas at a scanning pitch of 1 mm, a scanning speed of 10 mm/s, and a working distance of 20 mm between the glass tube and the specimen surface. Compared to the sample immediately after anodizing, the irradiated surface was found to have equivalent hydrophilicity, 88% of the fracture load in the tensile shear test, and 2% change from X-ray photoelectron spectroscopy (XPS) analysis of C and O on the specimen surface. Thus, short APPJ irradiation is effective in enhancing the surface wettability of specimens that have been exposed for more than 30 days from anodization. Furthermore, the surface modification technique attempted in this study can be applied to a wide range of multi-materials to enable the joining of dissimilar materials. © 2024 The Japan Institute of Light Metals.

关键词： Aluminum alloys

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Discussion Manipulation, Language and Domain Dependent Models: An Overview

Discussion Manipulation, Language and Domain Dependent Model...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Chalas, Filip Stupavsky, Igor Vranic, Valentino Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovakia

ISBN: (纸本)9798350347722

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site ***, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset-both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression-accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions. © 2023 IEEE.

关键词： Logistic regression

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd Science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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ISSUES IN USING REDUCED-SIZE SPECIMEN FOR BRITTLE FRACTURE EVALUATION OF BEAM-TO-COLUMN STEEL CONNECTIONS

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2022年第791期87卷 9-19页

作者： Oie, Naoya Kawabata, Tomoya Kishiki, Shoichi Nakagomi, Tadao Dept. of Systems Innovation Faculty of Engineering The University of Tokyo Japan Laboratory for Future Interdisciplinary Research of Science and Technology Institute of Innovative Research Tokyo Institute of Technology Japan Dept. of Architecture Faculty of Engineering Shinshu University Japan

The authors evaluate the effect of size on the brittle fracture initiation properties using proportionally sized specimens with 4:2:1 geometry, with the aim of clarifying the relationship between geometric shape (size) and brittle fracture properties. Three-point bending tests are conducted on eight types of specimens with different sizes and notch depths. Interpretations using simple beam theory or the traditional fracture mechanics parameter, crack tip opening displacement (CTOD) shows a large scale dependence. It is evident that an appropriately improved local approach is proposed to interpret the experimental results. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Brittle fracture

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Power harvesting footwear based on piezo-electromagnetic hybrid generator for sustainable wearable microelectronics

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Journal of King Saud University - engineering Sciences 2022年第5期34卷 329-338页

作者： Iqbal, Muhammad Khan, Farid Ullah Mehdi, Murtuza Cheok, Quentin Abas, Emeroylariffion Nauman, Malik Muhammad Energy Systems Engineering Faculty of Integrated Technologies Universiti Brunei Darussalam Jalan Tungku Link BE Gadong1410 Brunei Energy Harvesting Systems Research Lab Institute of Mechatronics University of Engineering and Technology Peshawar2500 Pakistan Department of Mechanical Engineering NED University of Engineering and Technology Karachi Pakistan

As wearable microelectronics become more ubiquitous, and the size and power requirements of such devices decrease, there is increasing research interest in harnessing power from ambient environmental sources through embedded systems as alternative to battery replacements. Accordingly, a multimodal hybrid piezo-electromagnetic insole energy harvester (PEM-IEH) has been presented in this paper as a means to reclaim the biomechanical energy wasted in the surroundings during daily walking. The hybrid device consists of two piezo-ceramic wafer plates, two magnets, and two wound coils. The designed harvester has been simulated, fabricated and experimentally validated. From the frequency response, the hybrid harvester exhibits four resonant frequencies concentrated around 8, 25, 50, and 51 Hz. A maximum combined power of 1400 µW is generated across the optimal load resistances of upper and lower electromagnetic generators at first resonant frequency (8 Hz) under 0.5 g base acceleration, and a 269 µW peak power is obtained across the optimum load resistances of the upper and lower piezoelectric generators at 4th resonance (51 Hz) under 0.5 g. The miniature (46.8 cm3) and lightweight (43.3 g) harvester was incorporated into the sole of an adult-sized commercial shoe, and has been shown to be able to charge a 100 µF capacitor, up to 2.4 Volt within approximately 10 min of slow jogging. The capacitor charging performance presents a remarkable potential application of the harvester in development of micro-power monitoring sensors and wearable microelectronic gadgets where batteries pose a practical bottleneck. © 2020 The Authors

关键词： Energy harvesting

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Deep learning application in diagnosing breast cancer recurrence

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Multimedia Tools and Applications 2025年第13期84卷 12265-12297页

作者： Jam, Zeinab Albadvi, Amir Atashi, Alireza Department of Information Technology Engineering-Industrial and Systems Engineering Faculty Tarbiat Modares University Tehran Iran Medical Informatics Department Breast Cancer Research Center Motamed Cancer Institute ACECR Tehran Iran Department of Digital Health School of Medicine Tehran University of Medical Sciences Tehran Iran

Patients' lives can always be saved when diseases, especially special diseases, are detected early. The chances of a patient surviving can be increased by early detection. Breast cancer is one of the deadliest and common cancers. After recovering from breast cancer, patients are always worried about recurrence and return. The use of modern technology, however, can help predict disease recurrence at an early stage, allowing patients to receive treatment sooner. Significant strides have been achieved in deep learning, demonstrating strong performance in handling unstructured data challenges. However, when it comes to predicting tabular data, deep learning hasn't quite matched its success with unstructured data. Presently, ensemble models relying on gradient-boosted decision trees (GBDT) are frequently favored for tabular data prediction tasks. Typically, these GBDT-based models outshine deep learning approaches. Many novel deep learning techniques are emerging for handling tabular data. TabNet, for instance, mirrors decision tree feature selection within a neural network framework. AutoInt addresses high dimensionality by condensing data through embedding layers. Tab Transformer adapts the transformer model, generating text representations for categorical attributes. Despite their innovation, these methods remain less recognized compared to those for image and text data processing. In this study, 158 different characteristics of 5142 breast cancer patients from 1997 to 2019 were examined. We aim to evaluate deep learning techniques effectiveness in detecting breast cancer recurrence. Through examination of evaluation metrics, it becomes evident that deep learning approaches applied to tabular data surpass traditional machine learning algorithms, even when dealing with imbalanced datasets. Ultimately, the results derived from each algorithm analyzed and concluded with a review and comparison of the findings. © The Author(s), under exclusive licence to Springer Science+

关键词： Diseases

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