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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3308 条记录，以下是601-610 订阅

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A Comparative Analysis of Deep Learning models for Detecting Malaria Disease through LBP Features 6

A Comparative Analysis of Deep Learning Models for Detecting...

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6th IEEE international conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022

作者： Zarima, Nona Muchtar, Kahlil Bintang, Akhyar Oktiana, Maulisa Maulina, Novi Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Aceh Indonesia School of Medicine Universitas Syiah Kuala Postgraduate Program Microbiology Department Aceh Indonesia

ISBN: (数字)9781665497428

ISBN: (纸本)9781665497428

Malaria is a parasitic infection spread by the plasmodium parasite. Malaria continues to be a major threat to world health, with an estimated 200 million cases and over 400,000 fatalities each year. When exposed to this disease, symptoms develop 10-15 days after the parasite enters the body. this disease becomes chronic if it is not treated medically, and it eventually leads to death. Using spatial information collected from microscopic images, several techniques based on image processing and machine learning have been utilized to diagnose malaria. Using the Local Binary Pattern (LBP) texture feature as a feature extraction approach, this study contributes to the development of a predictive and high-accuracy deep learning model by testing multiple Deep Learning models and determining which model delivers the best accuracy. To be specific, we tested frequently used baseline methods, namely ResNet34, VGG16, Inception V3, and EfficientNet. the results demonstrate that EfficientNet has a 91 percent outstanding accuracy rate, compared to 87 percent for VGG16, 81 percent for Resnet34, and 77 percent for InceptionV3, respectively. © 2022 IEEE.

关键词： Local binary pattern

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Momentum Analysis Based on Wavelet Neural Network: Inspiration from Quantum Physics and Artificial Intelligence

Momentum Analysis Based on Wavelet Neural Network: Inspirati...

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Jiaxuan Zhang Xianhao Zhan Xuewen Liu Collage of Physics Sichuan University Chengdu Sichuan China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

this study applies wavelet transform from quantum physics to neural networks to analyze the impact of momentum and other physical parameters on sports match outcomes. the model performed well in predicting the 2023 Carlos Alcaraz vs. Novak Djokovic match, with an MSE of 0.0004 on the test set, indicating high accuracy. By leveraging the wavelet transform's ability to handle non-stationary signals, we enhanced the neural network's predictive capabilities. these results demonstrate the promising applications of neural networks in effectively predicting sports match outcomes.

关键词： Wavelet transforms Accuracy Computational modeling Neural networks predictive models Prediction algorithms Market research

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Automatic Detection Method for software Requirements Text with Language Processing Model

Automatic Detection Method for Software Requirements Text wi...

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software engineering and Computer Science (CSECS), international conference on

作者： Li Zhang Liubo Ouyang Jiahao Qin Hunan University Changsha China

Requirements analysis plays a crucial role in the process of software development. However, due to many factors such as the dichotomy of natural language, requirement texts often present diverse defects, which may lead to anomalies and functional deviations in software systems. In order to solve the above challenges, our study proposes a deep learning-based defect detection method for requirement text, which utilizes deep learning text classification techniques, fully integrates the characteristics of requirement text defects, and designs two defect detection classification models, TextCNN and BERT, for comparative training and performance evaluation based on the construction of a defective text dataset, and constructs a deep learning neural network based on BERT suitable for requirement text defect detection. Experimental results show that the method achieves more than 90% accuracy in defect detection, effectively makes up for the shortcomings of traditional manual review, and significantly improves the efficiency of software development and the quality of requirement text.

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Separation of Concerns in Extended Epidemiological Compartmental models

Separation of Concerns in Extended Epidemiological Compartme...

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15th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2022

作者： Guifo Fodjo, A. Yvan Ziane, Mikal Stinckwich, Serge Anh, Bui thi Mai Bowong, Samuel CNRS UMR 7606 LIP6 Sorbonne Université Paris France Université de Paris Paris France United Nations University Institute in Macau China School of Information and Communication Technology Laboratory of Intelligent Software Engineering Hanoi University of Science and Technology Hanoi Viet Nam Département de Mathématiques Université de Douala Douala Cameroon IRD UMI 209 UMMISCO Bondy France URIFIA Université de Dschang Dschang Cameroon

Epidemiological models become more and more complex as new concerns are taken into account (age, sex, spatial heterogeneity, containment or vaccination policies, etc.). this is problematic because these aspects are typically intertwined which makes models difficult to extend, change or reuse. the Kendrick approach has shown promising results to separate epidemiological concerns but is restricted to homogeneous compartmental models. In this paper, we report on an attempt to generalize the Kendrick approach to support some aspects of contact networks, thereby improving the predictive quality of models with significant heterogeneity in the structure of contacts, while keeping the simplicity of compartmental models. this approach has been validated on two different techniques to generalize compartmental models. © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

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A Novel Inception Deep Learning Model for Mobile AI Stroke Prediction System: AI Research and Industry Perspectives for Connected Health

A Novel Inception Deep Learning Model for Mobile AI Stroke P...

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Computing and Informatics (ICCI), international conference on

作者： Bassant M. Elbagoury Rytis Maskeliuans Marwa Zaghow Nabil Kamel Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Ain Shams University Computer Science and Artificial Intelligence Programs King Salman International University KSIU Department of Multimedia Engineering Faculty of Informatics Kaunas University of Technology Lithuania Sales Business and Services Transformation Dell technologies EMEA Egypt Computer Science and Artificial Intelligence Programs Faculty of Computer Science and Engineering King Salma International university KSIU

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. the main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed.

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Hybrid Deep Learning Model for Copy Move Image Forgery Detection 6

Hybrid Deep Learning Model for Copy Move Image Forgery Detec...

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6th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Prabakar, D. Ganesan, R. Leela Rani, D. Neti, Praveen Kalyani, N. Mudradi, Shreesha Kalkoor Sns College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641035 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Sree Vidyanikethan Engineering College Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Aditya College of Engineering Department of Computer Science & Engineering Andhra Pradesh Surampalem533437 India R.M.K College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Thiruvallur601206 India Sambhram Institute of Technology Department of Electronics and Communication Engineering Karnataka Bangalore560097 India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Digital image processing software has progressed to the point where it is trivial to create forgeries by employing various manipulative tactics on the actual (real) images. Professions including law, health, and education are vulnerable to modified images. Hence, it is essential to be able to determine whether or not an image is fake. One form of image fraud is called 'copy-move,' in which a key object or objects are hidden from view by copying and pasting them into the same image. Many Copy-Move Forgery Detection (CMFD) models are rendered useless when the copied section contains noise or is resized before being pasted. Our goal of this research was to develop a highly effective and efficient detection method for this type of image forgery by utilizing a hybrid Deep Learning (DL) architecture. Firstly, the sample images are taken from MICCF2000. Second, alter the size of the images, and a filtering technique is used to eliminate any noise that may have been present in the original image. Finally, we combine Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) to build a hybrid DL model. Measures such as accuracy, True Positive (TPR) and Negative Rate (TNR), False Positive Rate (FPR) and Negative Rate (FNR), Precision, and F1-score are used to verify the developed hybrid DL model. Using the proposed hybrid DL model, we were able to detect fake images with an accuracy of 95%. © 2022 IEEE.

关键词： Deep learning

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BEMAO: A novel adaptive and high-order BEM solver for steady-state acoustics-part 1: theory 30

BEMAO: A novel adaptive and high-order BEM solver for steady...

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30th international conference on Noise and Vibration engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Atak, O. Li, Y. Hamiche, K. Beriot, H. Desmet, W. Siemens Digital Industries Software Francis House 112 Hills Rd CambridgeCB2 1PH United Kingdom Siemens Digital Industries Software Interleuvenlaan 68 Leuven3001 Belgium KU Leuven Department of Mechanical Engineering Celestijnenlaan 300 HeverleeB-3001 Belgium DMMS Core Lab Flanders Make Belgium

ISBN: (纸本)9789082893151

this paper presents a novel adaptive and high order implementation of the Boundary Element Method (BEM) for steady-state acoustics, which we refer to as BEMAO. the method uses a priori error estimators to automatically adapt the p-order of the elements based on the target accuracy, edge lengths and input frequency. Using high order interpolation shape functions (up to order 6) compresses the model size substantially. In addition, BEMAO is also able to use a single non-homogeneous mesh to cover a wide frequency range while it provides optimal model size per frequency without the need for remeshing. the assembly cost is reduced via the use of multipole approximations. A direct solver is used to solve the compressed models. All these properties make BEMAO a novel and competitive BEM solver. the Part 1 of the BEMAO papers focuses on the theoretical aspects: the quadrature rules, the convergence rates of the high orders and the accelerated assembly routines. the computational power and accuracy of BEMAO is demonstrated on academic examples. © 2022 proceedings of ISMA 2022 - international conference on Noise and Vibration engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Boundary element method

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Deep Learning Methods with Pre-Trained Word Embeddings and Pre-Trained Transformers for Extreme Multi-Label Text Classification 6

Deep Learning Methods with Pre-Trained Word Embeddings and P...

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6th international conference on Computer Science and engineering, UBMK 2021

作者： Erciyes, Necdet Eren Görür, Abdul Kadir Computer Engineering Dept. Çankaya University Ankara Turkey Software Engineering Dept. Çanicaya University Ankara Turkey

ISBN: (纸本)9781665429085

In recent years, there has been a considerable increase in textual documents online. this increase requires the creation of highly improved machine learning methods to classify text in many different domains. the effectiveness of these machine learning methods depends on the model capacity to understand the complex nature of the unstructured data and the relations of features that exist. Many different machine learning methods were proposed for a long time to solve text classification problems, such as SVM, kNN, and Rocchio classification. these shallow learning methods have achieved doubtless success in many different domains. For big and unstructured data like text, deep learning methods which can learn representations and features from the input data wtihout using any feature extraction methods have shown to be one of the major solutions. In this study, we explore the accuracy of recent recommended deep learning methods for multi-label text classification starting with simple RNN, CNN models to pretrained transformer models. We evaluated these methods' performances by computing multi-label evaluation metrics and compared the results with the previous studies. © 2021 IEEE

关键词： Embeddings

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Multimodal Recognition of Landmarks Based on Vision Language Model

Multimodal Recognition of Landmarks Based on Vision Language...

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international conference on Communications, Information System and Computer engineering (CISCE)

作者： Jinyu Mu Lianqiang Niu Yijiang Zhang School of Software Shenyang University of Technology Shenyang China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In the task of landmark recognition, the recognition accuracy is easily affected by factors such as lighting, angle, and season. Existing landmark recognition methods have poor ability to understand deep semantics and capture contextual relationships, resulting in low recognition accuracy. Moreover, some methods require complex preprocessing steps. To address the above issues, this paper proposes a multimodal landmark recognition method based on vision language model. the vision language pretraining model is used to make the model training more stable and accelerate the model convergence. the self-attention layer based on multi-scale fusion is used to adapt image features. Multimodal marker vectors are introduced to fine-tune prompt words, and feature fusion is carried out using gated cross-attention layer to make the model better understand the image-text correlation and improve the model prediction accuracy. the results of testing on the dataset show that the proposed method is superior to the current mainstream methods in terms of accuracy, and the accuracy of Top-1 and Top-5 can reach 80.72% and 92.16%.

关键词： Training Adaptation models Image recognition Accuracy Text recognition Computational modeling predictive models

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A software to Visualize, Edit, Model and Mesh Vascular Networks 44

A Software to Visualize, Edit, Model and Mesh Vascular Netwo...

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44th Annual international conference of the IEEE-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： DaToocq, Meghane Lavoue, Guillaume Ohta, Akoto Frindel, Carole Univ Lyon1 CREATIS INSA Lyon CNRS UMR5220INSERM U1206 F-69621 Villeurbanne France Univ Lyon 2 Univ Lyon UMR5205 LIRISINSA LyonCNRSUCBLCent Lyon F-69621 Villeurbanne France Univ Lyon UMR5205 LIRIS UCBLCent LyonCNRSINSA Lyon F-69130 Ecully France Univ Claude Bernard Lyon 1 Cent Lyon INSA Lyon CNRSELyTMaX IRL3757 Villeurbanne France Tohoku Univ Sendai Miyagi Japan Tohoku Univ Inst Fluid Sci 2-1-1 KatahiraAoba Ku Sendai Miyagi 9808577 Japan Tohoku Univ Grad Sch Biomed Engn 6-6 Aramaki Aza AobaAoba Ku Sendai Miyagi 9808579 Japan

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Computational fluid dynamics (CFD) is a key tool for a wide range of research areas, beyond the computer science community. In particular, CFD is used in medicine to measure blood flow from patient specific models of arteries. In this field, the creation of accurate meshes remains the most challenging step, as it is based on the segmentation of medical images, a time-consuming task which often requires manual intervention by medical doctors. In this context, user-friendly, interactive softwares are valuable. they enable to spread the new advances in numerical treatment to the medical community and enrich them with the expert knowledge (e.g anatomical knowledge) of clinicians. In this work, we present a user interface dedicated to the meshing of vascular networks from centerlines. It allows for the 3D visualization and edition of input centerlines, which constitute a simplified, easy-to-manipulate representation of vascular networks. the surface of the artery can be reconstructed from the modified centerlines by an editable parametric model and then meshed with high quality hexahedral elements. At every step of the process, the network can be confronted with medical images with enhanced visualization. the software will be released publicly.

关键词： Biomechanics Image segmentation Computational modeling Computational fluid dynamics Medical services User interfaces software

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