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2024 International Design Society Conference, Design 2024

作者： Gong, Zhengya Gonçalves, Milene Nanjappan, Vijayakumar Georgiev, Georgi V. Center for Ubiquitous Computing University of Oulu Finland Department of Design Organisation and Strategy Faculty of Industrial Design Engineering Delft University of Technology Netherlands School of Computer Science and Information Technology University College Cork Ireland

The relationship between culture and creativity has sparked the interest of researchers for decades. Although researchers have attempted to establish a connection between culture and creativity, the precise relationship between the two remains ambiguous. The current paper examined extant literature on the subject matter and synthesized the relations between culture and creativity in ideation over the past twenty years. The present study expounds upon the utilized samples, measures implemented to assess creativity and culture, and the study results. © 2024 Proceedings of the Design Society. All rights reserved.

关键词： Behavioral research

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TESTING IS LEARNING: USING RANDOMIZED, PARAMETRIZED, AND DIVERSIFIED WEEKLY ONLINE QUIZZES WITH ANSWER-DEPENDENT FEEDBACK IN MULTIVARIABLE CALCULUS 52

TESTING IS LEARNING: USING RANDOMIZED, PARAMETRIZED, AND DIV...

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52nd Annual Conference of the European Society for engineering: Educating Responsible Engineers, SEFI 2024

作者： Boussé, M. Delnoij, L. Jongen, S. Phillips, G. Department of Advanced Computing Sciences FSE Maastricht University Maastricht Netherlands Educational Research and Development SBE Maastricht University Maastricht Netherlands Maastricht Science Programme FSE Maastricht University Maastricht Netherlands Faculty of Science and Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9782873520274

In this paper, we present the development and evaluation of an online quiz system designed to enhance learning outcomes in multivariable calculus (MC) for engineering students. MC is crucial for understanding various engineering disciplines but often poses challenges due to its abstract nature. The quizzing system integrates formative and summative assessments, drawing on cognitive psychology research, and aligns with educational principles emphasizing active learning. Results indicate a positive correlation between engagement in practice quizzes and graded quizzes scores, supporting the system's effectiveness in improving student performance. However, student feedback suggests areas for improvement, particularly in quiz randomization and feedback adaptiveness. The study underscores the importance of integrating technology-enhanced learning tools into mathematics education, offering insights for future refinement and implementation in engineering curricula. © 2024 SEFI 2024 - 52nd Annual Conference of the European Society for engineering, Proceedings: Educating Responsible Engineers. All rights reserved.

关键词： Contrastive Learning

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On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI

On a Survival Gradient Boosting, Neural Network and Cox PH B...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Musto, Henry Stamate, Daniel Logofatu, Doina Ouarbya, Lahcen Goldsmiths College University of London Department of Computing United Kingdom Goldsmiths College University of London School of Health Sciences The University of Manchester Department of Computing United Kingdom Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Germany

ISBN: (纸本)9798350337488

In recent years, attention within the clinical prediction community has turned to the use of survival machine learning as a tool for predicting the risk of developing a disease as a function of time. The current work seeks to contribute to existing literature which demonstrates the utility of these methods when applied to a dementia prediction context. We use the Alzheimer's Disease Neuroimaging Initiative ADNI dataset and model deterioration within two distinct groups, those deemed cognitively normal and those with a formal diagnosis of Mild Cognitive Impairment. In agreement with existing literature we find that survival machine learning outperforms standard survival analysis methods such as Cox PH model, and has very good predictive ability. We propose an innovative approach to predicting dementia diagnosis risk on ADNI, which explores the use of survival neural network and survival extreme gradient boosting techniques that have hitherto seldom been applied to this context. The stability of our models was investigated within a Monte Carlo simulation framework. © 2023 IEEE.

关键词： Diagnosis

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Impact crater recognition methods:A review

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science China Earth sciences 2024年第6期67卷 1719-1742页

作者： Dong CHEN Fan HU Liqiang ZHANG Yunzhao WU Jianli DU Jiju PEETHAMBARAN College of Civil Engineering Nanjing Forestry UniversityNanjing 210037China Department of Geography State Key Laboratory of Remote Sensing ScienceFaculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210023China Key Laboratory of Space Object and Debris Observation Chinese Academy of SciencesNanjing 210023China Department of Mathematics and Computing Science Saint Mary’s UniversityHalifaxNS B3H 3C3Canada

Impact craters are formed due to the high-speed collisions between small to medium-sized celestial *** is the most significant driving force in the evolution of celestial bodies,and the impact craters provide crucial insights into the formation,evolution,and impact history of celestial *** this paper,we present a detailed review of the characteristics of impact craters,impact crater remote sensing data,recognition algorithms,and applications related to impact *** first provide a detailed description of the geometric texture,illumination,and morphology characteristics observed in remote sensing data of *** we summarize the remote sensing data and cataloging databases for the four terrestrial planets(i.e.,the Moon,Mars,Mercury,and Venus),as well as the impact craters on ***,we study the advancement achieved in the traditional methods,machine learning methods,and deep learning methods applied to the classification,segmentation,and recognition of impact ***,based on the analysis results,we discuss the existing challenges in impact crater recognition and suggest some ***,we explore the implementation of impact crater detection algorithms and provide a forward-looking perspective.

关键词： Terrestrial planets Deep space exploration Impact crater Impact crater recognition Deep learning

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Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2309-2321页

作者： Raghupathy, Bala Krishnan Vairavasundaram, Subramaniyaswamy Ganesan, Manikandan Namachivayam, Rajesh Kumar Kotecha, Ketan Herencsar, Norbert SASTRA Deemed University Department of CSE India Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India SASTRA Deemed University School of Computing India Symbiosis Centre for Applied Artificial Intelligence Pune India Brno University of Technology Faculty of Electrical Engineering and Communication Department of Telecommunications Technicka 3082/12 Brno61600 Czech Republic

Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain. © 2024 IEEE.

关键词： Convolutional neural networks

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Traffic Anomaly Detection Using Optimized Faster R-CNN

Traffic Anomaly Detection Using Optimized Faster R-CNN

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2024 International Conference on Electronics, Electrical and Information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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Investigation of the mesmeric effect of substituents in DSSC based on thiophene and oxadiazole. Structural and optoelectronic study using the DFT approach

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Interactions 2025年第1期246卷 1-15页

作者： Agda, Fatima Nebbach, Diae Louazri, Latifa Ninis, Ouafae Merimi, Imane Ajana, Mohammed Aziz Bouachrine, Mohammed Laboratory Molecular and Computational Chemistry: Design Synthesis and Reactivity. Department of Chemistry Faculty of Science Moulay Ismail University Meknes Morocco Regional Center for Education and Training Professions Meknes Morocco Energy Materials and Computing Physics Research Group ENS Abdelmalek Essaadi University Tetouan Morocco Advanced Materials and Process Engineering Laboratory Faculty of Science Ibn Tofail University Kenitra Morocco National School of Architecture National Road N2 Compus de Savoir Technolopole Oujda Oujda60000 Morocco Faculty of Sciences Mohammed First University Oujda Morocco

In this work, we have developed a range of organic Donor- π -Acceptor;we are changing the π-spacers by adding novel substitutions (F, CH3, and CN) to the base molecule D-TTOxTT-A for dye-sensitized solar cells (DSSCs). Our aim is to improve the photovoltaic performance of these engineered organic dyes. We use the B3LYP (d,p) and TD/CAM-B3LYP (d,p) to study the impact of different types of the introduction of the substitution in the spacer, on the sensitizer characteristics, with the aim of highlighting the links between structure and property. Based on these results, it is possible to use these dyes as promising sensitizers for DSSCs. In addition, M4 could be an ideal compound for high throughput DSSC due to its planar geometry, low energy gap (1.73eV), higher absorption wavelength (449.3nm), longer lifetime (2.366 ns), lower ΔGreg (-0.64 eV), large hardness (-0.869 eV) and reasonable energy reorganization (0.488eV). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Thiophene

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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Computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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Distributed Federated Split Learning Based Intrusion Detection System

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Intelligent Automation & Soft computing 2024年第5期39卷 949-983页

作者： Rasha Almarshdi Etimad Fadel Nahed Alowidi Laila Nassef Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHail55476Saudi Arabia

The Internet of Medical Things(IoMT)is one of the critical emerging applications of the Internet of Things(IoT).The huge increases in data generation and transmission across distributed networks make security one of the most important challenges facing IoMT *** Denial of Service(DDoS)attacks impact the availability of services of legitimate *** Detection Systems(IDSs)that are based on Centralized Learning(CL)suffer from high training time and communication *** that are based on distributed learning,such as Federated Learning(FL)or Split Learning(SL),are recently used for intrusion *** preserves data privacy while enabling collaborative model ***,FL suffers from high training time and communication *** the other hand,SL offers advantages in terms of computational resources,but it faces challenges such as communication overhead and potential security vulnerabilities at the split *** Split Learning(FSL)has proposed overcoming the problems of both FL and SL and offering more secure,efficient,and scalable distribution *** paper proposes a novel distributed FSL(DFSL)system to detect DDoS *** proposed DFSL enhances detection accuracy and reduces training time by designing an adaptive aggregation method based on the early stopping ***,the increased number of clients leads to increasing communication *** further propose a Multi-Node Selection(MNS)based Best ChannelBest l2-Norm(BC-BN2)selection scheme to reduce communication *** DL models are used to test the effectiveness of the proposed system,including a Convolutional Neural Network(CNN)and CNN with Long Short-Term Memory(LSTM)on two modern *** performance of the proposed system is compared with three baseline distributed approaches such as FedAvg,Vanilla SL,and SplitFed *** proposed system outperforms the baseline algorithms with an accuracy of 99.70%and 99.87%in CICDDoS20

关键词： IDS DFSL DDoS attacks CNN CNN+LSTM

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Building a neural speech recognizer for quranic recitations

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International Journal of Speech Technology 2023年第4期26卷 1131-1151页

作者： Al-Issa, Suhad Al-Ayyoub, Mahmoud Al-Khaleel, Osama Elmitwally, Nouh Department of Computer Engineering Jordan University of Science and Technology Irbid22110 Jordan Department of Computer Science Jordan University of Science and Technology Irbid22110 Jordan School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza12613 Egypt

This work is an effort towards building Neural Speech Recognizers system for Quranic recitations that can be effectively used by anyone regardless of their gender and age. Despite having a lot of recitations available online, most of them are recorded by professional male adult reciters, which means that an ASR system trained on such datasets would not work for female/child reciters. We address this gap by adopting a benchmark dataset of audio records of Quranic recitations that consists of recitations by both genders from different ages. Using this dataset, we build several speaker-independent NSR systems based on the DeepSpeech model and use word error rate (WER) for evaluating them. The goal is to show how an NSR system trained and tuned on a dataset of a certain gender would perform on a test set from the other gender. Unfortunately, the number of female recitations in our dataset is rather small while the number of male recitations is much larger. In the first set of experiments, we avoid the imbalance issue between the two genders and down-sample the male part to match the female part. For this small subset of our dataset, the results are interesting with 0.968 WER when the system is trained on male recitations and tested on female recitations. The same system gives 0.406 WER when tested on male recitations. On the other hand, training the system on female recitations and testing it on male recitation gives 0.966 WER while testing it on female recitations gives 0.608 WER. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Statistical tests

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