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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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Spatial Attention-Enhanced Encoder-Decoder Network for Accurate Segmentation of the Prostate’s Transition Zone

Spatial Attention-Enhanced Encoder-Decoder Network for Accur...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Dimitrios I. Zaridis Eugenia Mylona Nikolaos S. Tachos Charalampos Kalantzopoulos Kostas Marias Manolis Tsiknakis Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical & Computer Engineering and Biomedical Research Institute National Technical University of Athens and FORTH Athens & Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Research Institute and Unit of Medical Technology and Intelligent Information Systems FORTH University of Ioannina Ioannina Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Accurate segmentation of the prostate and its substructures consist the most important component for reliable localization and characterization of prostate cancer. In this study a Spatial Attention Residual U-Net (Spatial ResU-Net) deep learning (DL) network is proposed for segmenting the transitional zone of the prostate, by leveraging the learning capacity of spatial attention modules and residual connections. Spatial attention modules efficiently extract features in intra-channel manner and boost the performance of encoder and decoder while residual connections facilitate the information flow within the different network’s levels. The proposed model was compared against 8 state-of-the-art DL segmentation models demonstrating a superior performance. The improvement in terms of Sensitivity, Dice Score, Hausdorff distance and Average surface distance was at least 1%, 1%, 0.05 mm and 0.09 mm, respectively.

关键词： Location awareness Deep learning Accuracy Sensitivity Feature extraction Decoding Reliability Prostate cancer engineering in medicine and biology Testing

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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Quantifying Human Upper Limb Stiffness Responses Based on a Computationally Efficient Neuromusculoskeletal Arm Model

Quantifying Human Upper Limb Stiffness Responses Based on a ...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Maria Sapounaki Pierre Schumacher Winfried Ilg Martin Giese Christophe Maufroy Andreas Bulling Syn Schmitt Daniel F.B. Haeufle Isabell Wochner Institute of Computer Engineering (ZITI) Heidelberg University Germany Hertie Institute for Clinical Brain Research and Werner Reichard Centre for Integrative Neuroscience University of Tübingen Tübingen Germany Institut for Industrial Manufacturing and Management (IFF) University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Activities of daily living such as drinking and eating can be severely impaired for patients suffering from neurodegenerative diseases. One promising solution are assistive devices that apply corrective forces while still allowing the intended movements. However, real-time estimation of the required forces requires a detailed understanding of the limb's impedance characteristics. Here, we test and validate the stiffness response of a computationally efficient neuro-musculoskeletal arm model and its response to various force perturbations. We demonstrate that the arm model predicts stiffness characteristics that closely match experimental data recorded from humans and presents real-time applicability, allowing for implementation in practical scenarios and. Additionally, we predict the stiffness response for novel force levels and arm configurations. In the future, these predictions could be used to estimate corrective forces for assistive devices in real-time.

关键词： Computational modeling Perturbation methods Force Estimation Predictive models Manipulators Real-time systems Computational efficiency Assistive devices Impedance

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Uncovering neural scaling laws in molecular representation learning 23

Uncovering neural scaling laws in molecular representation l...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Dingshuo Chen Yanqiao Zhu Jieyu Zhang Yuanqi Du Zhixun Li Qiang Liu Shu Wu Liang Wang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Department of Computer Science University of California Los Angeles The Paul G. Allen School of Computer Science and Engineering University of Washington Department of Computer Science Cornell University Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research.

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High-Sensitivity Amino Acid Sensing Using Machine Learning-Optimized Graphene-Gold-Silver Metasurface-Based Surface Plasmon Resonance Biosensor in the Terahertz Regime

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Plasmonics 2025年 1-14页

作者： Umaeswari, P. Wekalao, Jacob Kaliaperumal, Kumaravel Department of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai India Department of Optics and Optical Engineering University of Science and Technology of China Hefei China Unit of Biomaterials Research Department of Orthodontics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

Traditional amino acid detection methods exhibit significant limitations including high costs, lengthy analysis times, and lack of portability. This study presents a metamaterial-enhanced graphene biosensor integrated with artificial intelligence for highly sensitive amino acid detection in the terahertz frequency range. Performance evaluation across 0.1–1 THz demonstrated exceptional sensitivity of 667GHzRIU−1, with transmittance values of 48.610–50.176% at resonant frequencies of 0.546–0.582 THz. Optimal performance occurred at 0.311 THz and 0.928 THz with minimal absorption losses. Random Forest regression algorithms achieved perfect R2 values (100%) for test cases within the 0.1–0.4 range, with R2 values improving from 91 to 100% as polynomial degrees increased from 2 to 10. The sensor demonstrates superior performance compared to existing solutions, offering a cost-effective, rapid, and highly accurate platform for amino acid detection with applications in clinical diagnostics, pharmaceutical development, and biotechnology.

关键词： Amino acid detection Artificial intelligence Graphene Metamaterial biosensor Terahertz spectroscopy

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Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

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Frontiers in Energy research 2024年 12卷

作者： Alhussan, Amel Ali Khafaga, Doaa Sami El-kenawy, El-Sayed M. Eid, Marwa M. Ibrahim, Abdelhameed Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia MEU Research Unit Middle East University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences. Copyright © 2024 Alhussan, Khafaga, El-kenawy, Eid and Ibrahim.

关键词： Charging stations

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Real-time Protection Against Microgrid False Data Injection Attacks Using Passive Monitoring

Real-time Protection Against Microgrid False Data Injection ...

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IEEE General Meeting Power& Energy Society

作者： Mark Karanfil El-Nasser S. Youssef Marthe Kassouf Mourad Debbabi Mohsen Ghafouri Aiman Hanna Security Research Center Gina Cody School of Engineering and Computer Science Concordia University Systems Resiliency Unit Hydro-Québec’s Research Institute (IREQ) Montréal Québec Canada

Microgrids are continuing to play a big role in the smart grids by offering higher reliability and integration of Distributed Energy Resources (DERs). Due to their dependence on accurate and authenticated data, microgrids subjected to False Data Injection (FDI) attacks can have their ability to provide energy negatively impacted. In this paper, a real-time centralized monitoring scheme is proposed to detect and mitigate FDI attacks that falsely portray a line fault. Passive monitoring devices are deployed in multiple regions of the microgrid, and data-driven neural network techniques are used to compute the extent that line measurement inferred from incoming IEC 61850 Sampled Value (SV) frames deviate from expected operation. The deviations are reported to a central security manager, which determines whether or not an FDI attack is underway based on the amount of inconsistency in the reported deviations. The impact of FDI attacks that present the microgrid as being under fault is demonstrated on a microgrid co-simulation testbed. The proposed centralized detection approach is found to be effective at identifying fault FDI attacks in cases where a minority subset of sensors is targeted.

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Model-Based Analysis of Sustainable Energy Transition: A Case Study of Portugal's Regional Wind and Solar Power Generation

Model-Based Analysis of Sustainable Energy Transition: A Cas...

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International Conference on European Electricity Market, EEM

作者： André Rodrigues de Oliveira Salvador Doménech Martínez José Villar Collado Mónica Meireles Max Alberto López-Maciel Fátima Lima Edimar Ramalho Margarita Robaina Mara Madaleno Marta Ferreira Dias INESC TEC –Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISCTE-University Institute of Lisbon and Business Research Unit (bru_iscte) Lisboa Portugal DAO & CESAM University of Aveiro Aveiro Portugal DEGEIT – Department of Economics Management Industrial Engineering and Tourism GOVCOPP – Research Unit on Governance Competitiveness and Public Policies University of Aveiro Aveiro Portugal

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

In the context of the R3EA project, funded by the Portuguese Foundation for Science and Technology (FCT), we analyse a set of selected future power system scenarios to assess the impact, on the Iberian electricity market (MIBEL), of installing wind and solar generation capacity in Portugal's Centro Region. We use the long-term MIBEL operation and planning model CEVESA. The scenarios are designed based on the current economic situation and the last National Energy and Climate Plan drafts for Portugal and Spain, by distributing the expected new wind and solar generation capacity differently among Portugal regions, also considering the flexible demand for producing electrolytic hydrogen. Market prices, capture prices and production per technology are analysed to assess this impact. Results show that regional investments have no significant impact on the MIBEL variables analysed.

关键词： Wind energy generation Renewable energy sources Analytical models Sensitivity Production Wind power generation Power systems

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Deep Trajectory Classification Model for Congestion Detection in Human Crowds

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computers, Materials & Continua 2021年第7期68卷 705-725页

作者： Emad Felemban Sultan Daud Khan Atif Naseer Faizan Ur Rehman Saleh Basalamah Department of Computer Engineering College of Computing and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science National University of TechnologyIslamabadPakistan Science and Technology Unit Umm Al-Qura UniversityMakkahSaudi Arabia Institute of Consulting Research and Studies Umm Al-Qura UniversityMakkahSaudi Arabia

In high-density gatherings,crowd disasters frequently occur despite all the safety *** detection of congestion in human crowds using automated analysis of video footage can prevent crowd *** work on the prevention of crowd disasters has been based on manual analysis of video *** methods also measure crowd congestion by estimating crowd ***,crowd density alone cannot provide reliable information about *** paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian *** proposed framework divided the input video into several temporal *** then extracted dense trajectories from each temporal segment and converted these into a spatio-temporal image without losing information.A classification model based on convolutional neural networks was then trained using spatio-temporal ***,we generated a score map by encoding each point trajectory with its respective class *** this,we obtained the congested regions by employing the non-maximum suppression method on the score ***,we demonstrated the proposed framework’s effectiveness by performing a series of experiments on challenging video sequences.

关键词： Crowd congestion trajectory classification crowd analysis

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