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parallel processing and applied mathematics: 11th international conference, PPAM 2015 Krakow, Poland, september 6-9, 2015 revised selected papers, part I

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9573卷

作者： Wyrzykowski, Roman Deelman, Ewa Dongarra, Jack Karczewski, Konrad Kitowski, Jacek Wiatr, Kazimierz Czestochowa University of Technology Czestochowa Poland Department of Computer Science University of Southern California Marina Del ReyCA United States Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Institute of Computer and Information Science Czestochowa University of Technology Czestochowa Poland Department of Computer Science AGH University of Science and Technology Krakow Poland AGH University of Science and Technology Krakow Poland

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Intensity edge processing of Moroccan sign language video frames using Sobel edge detection and thresholding techniques

Intensity edge processing of Moroccan sign language video fr...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Abdelbasset Boukdir Mohamed Benaddy Othmane El Meslouhi Mustapha Kardouchi Moulay Akhloufi Computer Science Department FPO LabSI Ibn Zohr University Agadir Morocco LMPEQ Laboratory ENSA - Safi Cadi Ayyad University Marrakesh Morocco Computer Science Department PRIME Laboratory Moncton University Moncton Canada

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

this paper presents an edge intensity processing framework applied to Moroccan sign language video frames using Sobel edge detection and hysteresis thresholding. the methodology consists of pre-processing video images by converting them to grayscale, then applying the Sobel operator to extract edge features. Intensity thresholds are set to classify pixels as low, high or hysteresis-based edges, to improve gesture boundary detection. the framework calculates average edge intensities per row and visualizes them for comparative analysis. Experimental results demonstrate the robustness of the proposed approach in identifying significant gesture features, supporting potential applications in sign language recognition and translation systems. the study highlights the effectiveness of contour-based methods for gesture based pattern selection in real-world sign language datasets.

关键词： Learning systems Sign language thresholding (Imaging) Translation Image edge detection Scalability Noise Feature extraction Robustness Hysteresis

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Enhancing Medical Education in Morocco: A Conceptual Framework for an ePortfolio System

Enhancing Medical Education in Morocco: A Conceptual Framewo...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Oussama Badouri Salem Nafiri Abdelhak Elidrissi Mariam Cherrabi Mohammed Mouhaoui Faculty of Medicine and Pharmacy Hassan II University Casablanca Morocco LaGeS Laboratory Modeling and Numerics Team Hassania School for Public Works Casablanca Morocco Rabat Business School International University of Rabat Rabat Morocco Faculty of Medicine and Pharmacy of Casablanca Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

this study proposes a conceptual ePortfolio system designed to enhance the learning outcomes of final-year medical students in Morocco. Grounded in a constructivist approach and leveraging the potential of Web 3.0 technologies, the framework aims to address the critical need for a dynamic and innovative approach to documenting, processing, and leveraging clinical experiences. By bridging the gap between theoretical knowledge and practical application, the framework promotes reflective practice, enhances feedback and collaboration, and equips students for a seamless transition into independent medical practice. this approach highlights the transformative potential of ePortfolios in shaping competent, reflective, and adaptable healthcare professionals.

关键词： Semantic Web Education Unified modeling language Collaboration Medical services

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Optimizing Training Hyperparameters for Multilayer Perceptrons in Deep Learning

Optimizing Training Hyperparameters for Multilayer Perceptro...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Safaa Echamsi Essadik Belouafi Aziza El Bakali Kassimi Abdelhakim Alali Abdelaziz Bouroumi Asmae Guennoun Information Processing Laboratory Faculty of Sciences Ben M’Sick Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Deep neural networks require an appropriate selection of hyperparameters for their operation. Among these, training hyperparameters, such as the learning rate, batch size, or the parameters of optimization algorithms, play an important role in the models’ convergence, accuracy, and generalization ability. this paper reviews the most influential training hyperparameters in the context of multilayer perceptrons (MLPs). An analysis of recent studies highlights the approaches used to optimize these hyperparameters and their impacts on model performance. Finally, this paper explores current challenges related to the optimization of training hyperparameters and suggests future research directions.

关键词： Training Deep learning Analytical models Accuracy Reviews Artificial neural networks Multilayer perceptrons Optimization Convergence

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Optimized Deep Learning Model for Early Parkinson’s Disease Detection Using Speech Spectrograms

Optimized Deep Learning Model for Early Parkinson’s Disease...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Oumaima Majdoubi Asma Ouabd Abdelilah Jilbab Achraf Benba Ahmed Hammouch E2SN Research Team ENSAM-R Mohammed V University in Rabat Rabat Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, and early detection is crucial for timely intervention and management. this study proposes an innovative approach for non-invasive PD detection using deep learning techniques applied to speech analysis. Speech recordings of Parkinson’s patients and healthy individuals were converted into spectrograms using the Short-Time Fourier Transform (STFT), which were then processed using Principal Component Analysis (PCA) for dimensionality reduction. We employed several convolutional neural network (CNN) architectures, including CNN, MobileNet, ResNet50, and Inception, to classify the data. A 5-fold cross-validation was applied. Among the models, CNN demonstrated superior performance, achieving 99.19% accuracy, 100% precision, and 99.21% F1 score without cross-validation, which improved to perfect scores across all metrics when cross-validation was applied. the results indicate that deep learning, coupled with speech signal processing and cross-validation, provides a powerful tool for the early, non-invasive detection of Parkinson’s disease. this approach holds significant potential for improving diagnosis accuracy and enabling timely medical intervention.

关键词： Deep learning Accuracy Speech analysis Convolutional neural networks Artificial intelligence Speech processing Spectrogram Diseases Principal component analysis Residual neural networks

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A Hybrid Computational Framework Using NLP and ML for Emotion Analysis in Sinhala Songs

A Hybrid Computational Framework Using NLP and ML for Emotio...

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Advanced Research in Computing (ICARC), international conference on

作者： Malinda Peiris Deshan Sumanathilaka School of Computing Informatics Institute of Technology Colombo Sri Lanka School of Mathematics and Computer Science Swansea University Wales United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

this study Rasathura explores the emotional dimensions of Sri Lankan music, specifically Sinhala songs, by developing an emotional classification model using both audio features and lyrical analysis. the research addresses the limitations in emotional classification using advanced Natural Language processing (NLP) techniques for Sinhala music. For audio analysis, JAudio was used to extract musical data, and machine learning (ML) techniques, including Random Forest, Sequential Minimal Optimization (SMO), Naive Bayes, Decision Tree, and Logistic Regression, were evaluated for classification. Relief-based and correlation-based attribute selection were utilized to enhance classification accuracy. the SMO algorithm achieved an accuracy of 75.92% using JAudio features, which increased to 79.01% with Relief-based attribute selection. the BERT model (built on XLM-RoBERTa) was used for lyrical analysis, achieving a maximum accuracy of 61.22%. TFIDF, Word2Vec with n-grams, and BERT embeddings were evaluated for feature extraction in lyrical data. the findings could benefit the future music industry by creating personalized music recommendation systems, enhancing music therapy applications, and contributing to preserving cultural heritage through digital music archiving.

关键词： Analytical models Accuracy Machine learning algorithms Music Medical treatment Feature extraction Natural language processing Recommender systems Random forests Optimization

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Development of an ultra-wideband patch antenna in the form of a decagon for mobile applications

Development of an ultra-wideband patch antenna in the form o...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Achennani Ahmed Youssef El Merabet Mohamed Marzouk Youssef Rhazi Department of Physics Faculty of Sciences Laboratory Electronic Systems Information Processing Mechanics and Energetics (SETIME) Ibn Tofail University Kenitra Morocco Microelectronics Embedded Systems and Telecommunications (MiSET) Faculty of Sciences and Technology Beni-Mellal Morocco

this paper is based on the design of a 405;305;1.6 mm 3 decagonal multiband patch antenna with a copper body and a 1.6 mm thick FR4 substrate for consistent electrical properties and thermal stability. It is d... 详细信息

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

this paper is based on the design of a 40×30×1.6 mm 3 decagonal multiband patch antenna with a copper body and a 1.6 mm thick FR4 substrate for consistent electrical properties and thermal stability. It is designed using HFSS software for UWB mobile applications, with a frequency band between 2.02 GHz and 11.12 GHz. the innovative design allows the antenna to be compact while covering a wide range of frequencies for all WLAN applications. the antenna can operate with multiple telecommunications standards, namely, LTE, RFID, Bluetooth, WLAN, Wi-MAX, C-band, and a portion of the X-band.

关键词： Bluetooth Patch antennas Bandwidth WiMAX Radar tracking Mobile applications Ultra wideband radar thermal stability Wireless fidelity Substrates

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Exploring Techniques and Countermeasures Against Browser Tracking: A Comprehensive Survey

Exploring Techniques and Countermeasures Against Browser Tra...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Malak Ourrahte Ahmed El-Yahyaoui Fatima-Ezzahra Ziani Intelligent Processing and Security of Systems(IPSS) Faculty of Sciences Mohammed V University in Rabat Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Considering the scale of online privacy threats, it is crucial to investigate the effectiveness of current means of protecting users against these malicious methods. this paper examines recently developed techniques for preventing websites from tracking users' browsing habits without consent. We review tracking techniques based on client storage, such as cookies, including but not limited to supercookies and third-party cookies, in addition to HTML5 storage. We also present an advanced technique known as fingerprinting. the latter does not require storage on the device, like cookies, making it more difficult to bypass. Particular attention is given to behavioral tracking tools like mouse tracking. We talk about the legal rules and regulations that are currently in place. We also explain how it is possible to defend against them with the privacy protections of browsers (Firefox, Safari, and others.) and some extensions (uBlock Origin, AdBlock Plus, among others.) while examining the limits of these solutions.

关键词： Surveys Privacy Reviews Law Fingerprint recognition Regulation Browsers Protection Artificial intelligence Faces

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Advanced Software-in-the-Loop Simulation Environment for UAV Control Algorithms

Advanced Software-in-the-Loop Simulation Environment for UAV...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Ismail Laabousse Mohamed Haidoury Younes El Bouzekri El Idrissi Engineering Sciences Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco Modeling Information Processing and Control Systems National School of Arts and Crafts Moulay Ismail University Meknes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

the rapid adoption of Unmanned Aerial Vehicles (UAVs) highlights the necessity for a robust testing environment to ensure the reliability, safety, and efficiency of control algorithms. Software-in-the-Loop (SIL) simulation constitutes a critical step in the validation of these systems prior to their deployment in Hardware-in-the-Loop (HIL) simulations or real-world flight trials. this study introduces the design and the implementation of a SIL simulation platform for UAV applications. the proposed environment integrates X-Plane 12 as a high-fidelity flight dynamics simulation engine, Python-based implementation of control algorithms, and a custom-designed Ground Control Station (GCS). the simulator enables performance evaluation of control algorithms throughout various trajectories and under diverse scenarios. Simulation outcomes validate the capability of the SIL simulation environment to accurately replicate UAV flight dynamics and generate meaningful insights into the performance and robustness of tested control algorithms.

关键词： Machine learning algorithms Heuristic algorithms Autonomous aerial vehicles Robustness Real-time systems Trajectory Synchronization Vehicle dynamics Engines Testing

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Research on ML-based Multi-model Comparison & Feature Importance Analysis in Corporate Credit Scoring Models 24

Research on ML-based Multi-model Comparison & Feature Import...

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Proceedings of the 2024 5th international conference on Big Data Economy and Information Management

作者： Yan Yan Jiwen Li Jing Yan Bingheng Wu Gubiao Fu Department of Health Services and Management Dalian Neusoft University of Information Dalian Liaoning China Department of Intelligent Medicine and Information Engineering Dalian Neusoft University of Information Dalian Liaoning China Finance Department CRRC Dalian Locomotive & Rolling Stock Co. Ltd. Dalian Liaoning China

ISBN: (纸本)9798400711862

In this study, a prediction model is constructed based on a comprehensive corporate information and business conditions dataset to predict the risk of corporate customers. the model will be applied to credit assessment, loan approval, risk management, and business intelligence analysis to provide scientific decision support for financial institutions and enterprises. the study explores the impact of features, deals with missing data and outliers, and compares different machine learning models to determine the best risk prediction model. the employment of these methodologies enables the effective identification and prevention of potential risks in enterprises, thereby enhancing their risk management capabilities.

关键词： Data processing

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