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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Charef, Ayoub Jarir, Zahi Quafafou, Mohamed LAMIGEP EMSI Moroccan School of Engineering Marrakesh Morocco Department of Computer Science Faculty of Science Cadi Ayyad University Marrakech Morocco Department of Computer Science University of Aix-Marseille Marseille France

ISBN: (纸本)9783031704109

This study investigates the impact of motorcycles on traffic dynamics at a signalized intersection in Marrakech, focusing on car speed, travel time, and intersection length. Utilizing VISSIM simulation, three scenarios were examined: without motorcycles, with 50% motorcycles, and with 100% motorcycles. The results reveal that the presence of motorcycles, even at a moderate level, influences car speed, leading to a slight reduction in average speed in the 50% scenario. In the 100% scenario, a more pronounced impact is observed, with a substantial decrease in car speed, increased travel time, and altered intersection length, indicative of potential congestion. These findings underscore the importance of considering motorcycles in traffic simulations for effective urban planning and traffic management. The study contributes valuable insights into the complex interactions between motorcycles and cars, emphasizing the need for comprehensive models to accommodate diverse vehicle types. Acknowledging the role of motorcycles in urban mobility is crucial for devising strategies that enhance traffic efficiency and safety in dynamic urban environments like Marrakech. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motorcycles

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An Integrated Deep Learning Framework for Fruits Diseases Classification

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computers, Materials & Continua 2022年第4期71卷 1387-1402页

作者： Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science Bahria UniversityIslamabad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

:Agriculture has been an important research area in the field of image processing for the last five *** affect the quality and quantity of fruits,thereby disrupting the economy of a *** computerized techniques have been introduced for detecting and recognizing fruit ***,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the ***,we propose an integrated deep learning framework for classifying fruit *** consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and *** proposed method comprises several important ***,data increase is applied,and then two different types of features are *** the first feature type,texture and color features,i.e.,classical features,are *** the second type,deep learning characteristics are extracted using a pretrained *** pretrained model is reused through transfer ***,both types of features are merged using the maximum mean value of the serial ***,the resulting fused vector is optimized using a harmonic threshold-based genetic ***,the selected features are classified using multiple *** evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

关键词： Fruit diseases data augmentation deep learning classical features features fusion features selection

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Segmentation Of Intracerebral Haemorrhage CT-Scan Images Using N-Net Architecture 3

Segmentation Of Intracerebral Haemorrhage CT-Scan Images Usi...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Santhi, P. Maheswari, S. Deepa, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tiruchirapalli Avadi India

ISBN: (纸本)9798350369083

In recent years, image segmentation has emerged as a critical task in computer vision and various applications such as medical image processing, autonomous driving, and satellite imagery analysis. Deep learning techniques, particularly convolutional neural networks (CNNs), have played a pivotal role in revolutionizing image segmentation by offering unprecedented accuracy and efficiency. Many algorithms have used for the segmentation such as U-Net, V-Mesh, U-Net++ and etc. But, U-Net having the challenges in parameter transfer, model size and in speed. To overcome these issues, this paper explores the evolution of image segmentation techniques into ICH (Intracerebral Haemorrhage) for better diagnosis. This model is tested using ICH dataset available in Kaggle. In which, N-Net have produced the better performance in terms of precision, Dice, Recall and F1 Score. © 2024 IEEE.

关键词： Mesh generation

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Diagnostic Classification of Heart Disease Using Boosting Algorithms 4th

Diagnostic Classification of Heart Disease Using Boosting Al...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Srivastava, Dolley Pandey, Himanshu Agarwal, Ambuj Kumar Sharma, Richa Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology University of Lucknow Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819937158

Heart disease is rapidly overtaking other causes of death in India, and it is a major threat to both men and women. Among the top causes of mortality throughout the globe, heart disease ranks first. Therefore, it is crucial to accurately and rapidly forecast cardiac problems at an early stage to guarantee the lives of millions of people. In the realm of medicine, machine learning has become more important. The realm of machine learning is where the idea of boosting first appeared. The underlying premise is that by merging multiple cases into a more accurate forecast, the accuracy of a rather ineffective classifying tool may be improved. Subsequently, this overarching idea was implemented in statistical modeling. The purpose of this research is to evaluate and contrast the performance of four popular boosting algorithms for machine learning in the context of cardiac illness diagnosis and prediction. Test results show that among the models evaluated, XGBoost performed best in terms of classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

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Automated Classification and Segmentation of Brain Tumor Images using Backboned ResUNet Framework 6

Automated Classification and Segmentation of Brain Tumor Ima...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Prasanalakshmi, B. Anitha, Cuddapah Balamanigandan, R. Mahaveerakannan, R. Gade, N.V. Ratnakishor Adjunct Faculty Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

One of the most challenging issues in computer imaging is the automated segmentation of brain tumors using Magnetic Resonance Images (MRI). Several approaches are explored using Deep Neural Networks in image segmentation since they perform well in automated tumor segmentation using tumor scans. Due to the complexity, training deeper neural networks usually takes a long time and many processing resources. An automated approach for classifying and segmenting images of Brain tumor MRI is proposed. As a hybrid transfer learning approach, modified ResNet50 identifies the class of MRI images as Benign or malignant. After identifying the Benign images, the tumor’s location is segmented using a hybrid network involving ResUNet with the Xception network as Backbone. This combination approach is chosen after its comparative analysis with other backbones. Various combination approaches are carried out, and performance metrics like Jaccard, Dice, and Tversky coefficient are done to prove the ResUNet backboned with Xception plays a better role concerning accuracy and speed. The proposed approach was tested on the dataset Cancer Imaging Archive to validate its efficiency. The proposed model has an average dice similarity coefficient score of 0.94, outperforming the conventional transfer learning training method with all the layers. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Sizing electrolyzer capacity in conjunction with an off-grid photovoltaic system for the highest hydrogen production

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Energy Harvesting and Systems 2023年第2期10卷 331-348页

作者： Hassan, Qusay Abbas, Majid K Tabar, Vahid Sohrabi Tohidi, Sajjad Abdulrahman, Imad Saeed Salman, Hayder M. Department of Mechanical Engineering University of Diyala Baqubah Iraq Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq

The electrolysis of renewable energy to produce hydrogen has become a strategy for supporting a decarbonized economy. However, it is typically not cost-effective compared to conventional carbon-emitting methods. Due to the predicted intermediate of low-and zero-marginal-cost renewable energy sources, the ability of electrolysis to connect with electricity pricing offers a novel way to cost reduction. Moreover, renewables, particularly photovoltaics, have a deflationary effect on the value of the grid when they are deployed. This study investigates solar electrolysis configurations employing photovoltaic cells to feed a proton exchange membrane water electrolyzer for hydrogen production. Using experimental meteorological data at 1-min precision, the system has been evaluated in Baghdad, the capital of Iraq. Positioned at the yearly optimum tilt angle for the selected site, the solar array is rated at 12 kWp. Temperature effects on solar module energy loss are taken into account. Several electrolyzers with capacities ranging from 2 to 14 kW in terms of hydrogen production were examined to determine the efficacy and efficiency of renewable sources. MATLAB was utilized for the simulation procedure, with a 2021-2035 project lifespan in mind. The results suggest that a variety of potentially cost-competitive options exist for systems with market configurations that closely approximate wholesale renewable hydrogen. At 4313 h of operation per year, the planned photovoltaic array generated 18,892 kWh of energy. The achieved hydrogen production cost ranges between & amp;dollar;5.39/kg and & amp;dollar;3.23/kg, with an ideal electrolyzer capacity of 8 kW matching a 12 kWp photovoltaic array capable of producing 450 kg/year of hydrogen at a cost of & amp;dollar;3.23/kg. & amp;copy;2022 Walter de Gruyter GmbH, Berlin/Boston. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Electrolysis

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A Review of Grid-Connected Inverters and Control Methods Under Unbalanced Grid Conditions 16

A Review of Grid-Connected Inverters and Control Methods Und...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Marangalu, Milad Ghavipanjeh Rehman, Fazal Ur Kurkdjian, Naser Vosoughi Computer Queen's University Dep. Of Electrical and Computer Engineering KingstonON Canada University of L'Aquila Dep. Information Engineering Computer Science and Mathematics L'Aquila Italy San Diego State University Faculty of Electrical and Computer Engineering San DiegoCA92182-0001 United States

ISBN: (纸本)9798331533946

Grid-connected inverters play a pivotal role in integrating renewable energy sources into modern power systems. However, the presence of unbalanced grid conditions poses significant challenges to the stable operation of these inverters. This review paper provides a comprehensive overview of grid-connected inverters and control methods tailored to address unbalanced grid conditions. Beginning with an introduction to the fundamentals of grid-connected inverters, the paper elucidates the impact of unbalanced grid voltages on their performance. Various control strategies, including voltage and current control methods, are examined in detail, highlighting their strengths and limitations in mitigating the effects of grid imbalance. Lastly, the review identifies emerging trends and future research directions in the field, emphasizing the need for innovative solutions to address the evolving challenges posed by unbalanced grid conditions in the context of renewable energy integration. © 2025 IEEE.

关键词： control strategy fault conditions grid-connected inverters Photovoltaic systems

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An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

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computers, Materials & Continua 2023年第10期77卷 665-680页

作者： Sidra Abbas Gabriel Avelino Sampedro Shtwai Alsubai Ahmad Almadhor Tai-hoon Kim Department of Computer Science COMSATS UniversityIslamabadPakistan College of Computer Studies De La Salle UniversityManila1004Philippines Faculty of Information and Communication Studies University of the Philippines Open UniversityLos Banos4031Philippines College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia School of Electrical and Computer Engineering Yeosu CampusChonnam National UniversityYeosu-si59626Korea

Cardiac disease is a chronic condition that impairs the heart’s *** includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart *** conditions can lead to serious complications and even be life-threatening if not detected and managed in *** have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and *** approaches have been applied to predict and treat heart disease utilizing ML and *** paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease *** approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart *** models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart ***-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed *** reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.

关键词： Deep neural network heart disease healthcare machine learning stacking

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A New Approach for Quantum Phase Estimation Based Algorithms for Machine Learning 8th

A New Approach for Quantum Phase Estimation Based Algorithm...

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8th International Conference on Smart City Applications, SCA 2023

作者： Ouedrhiri, Oumayma Banouar, Oumayma El Hadaj, Salah Raghay, Said Department of Computer Science and Mathematics University of Quebec at Trois-Riviéres Trois-Riviéres Canada Faculty of Sciences and Techniques Laboratory of Computer Science Engineering and Systems Marrakesh Morocco

ISBN: (纸本)9783031543753

One of the greatest developments in computer science is undoubtedly quantum computing. It has demonstrated to give various benefits over the classical algorithms, particularly in the significant reduction of processing time, due to the parallelism and entanglement properties. One of the most crucial quantum computing algorithms is quantum phase estimation (QPE). It is called the eigenvalue finding module for unitary operators. It has helped to solve the order finding and the factoring problem, and to calculate the eigenvalues of unitary matrices and quantum sampling methods. In this paper, we study recent improved versions for the QPE procedure, their advantages and experimentation. We also propose a new approach for QPE based algorithms for machine learning (ML). These algorithms are the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, the quantum singular value thresholding (QSVT) algorithm for matrix completion in recommender systems, and the quantum principal components analysis (QPCA) for data visualization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting 26

Assessing Predictor Influence in LSTM Models for Enhanced So...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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