Relevant bibliographies by topics / Algorithme MIIC

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Academic literature on the topic 'Algorithme MIIC'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Algorithme MIIC"

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Weyerer, Veronika, Pamela L. Strissel, Reiner Strick, Danijel Sikic, Carol I. Geppert, Simone Bertz, Fabienne Lange, et al. "Integration of Spatial PD-L1 Expression with the Tumor Immune Microenvironment Outperforms Standard PD-L1 Scoring in Outcome Prediction of Urothelial Cancer Patients." Cancers 13, no. 10 (May 12, 2021): 2327. http://dx.doi.org/10.3390/cancers13102327.

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Background: Immune therapy has gained significant importance in managing urothelial cancer. The value of PD-L1 remains a matter of controversy, thus requiring an in-depth analysis of its biological and clinical relevance. Methods: A total of 193 tumors of muscle-invasive bladder cancer patients (MIBC) were assessed with four PD-L1 assays. PD-L1 scoring results were correlated with data from a comprehensive digital-spatial immune-profiling panel using descriptive statistics, hierarchical clustering and uni-/multivariable survival analyses. Results: PD-L1 scoring algorithms are heterogeneous (agreements from 63.1% to 87.7%), and stems from different constellations of immune and tumor cells (IC/TC). While Ventana IC5% algorithm identifies tumors with high inflammation and favorable baseline prognosis, CPS10 and the TCarea25%/ICarea25% algorithm identify tumors with TC and IC expression. Spatially organized immune phenotypes, which correlate either with high PD-L1 IC expression and favorable prognosis or constitutive PD-L1 TC expression and poor baseline prognosis, cannot be resolved properly by PD-L1 algorithms. PD-L1 negative tumors with relevant immune infiltration can be detected by sTILs scoring on HE slides and digital CD8+ scoring. Conclusions: Contemporary PD-L1 scoring algorithms are not sufficient to resolve spatially distributed MIBC immune phenotypes and their clinical implications. A more comprehensive view of immune phenotypes along with the integration of spatial PD-L1 expression on IC and TC is necessary in order to stratify patients for ICI.
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Pourkashani, Ava, Asadollah Shahbahrami, and Alireza Akoushideh. "Copy-move forgery detection using convolutional neural network and K-mean clustering." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2604. http://dx.doi.org/10.11591/ijece.v11i3.pp2604-2612.

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Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.
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Tinnathi, Sreenivasu, and G. Sudhavani. "Copy-Move Forgery Detection Using Superpixel Clustering Algorithm and Enhanced GWO Based AlexNet Model." Cybernetics and Information Technologies 22, no. 4 (November 1, 2022): 91–110. http://dx.doi.org/10.2478/cait-2022-0041.

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Abstract In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameters of AlexNet. Based on the obtained features, the adaptive matching algorithm is used for locating the forged regions in the tampered images. Simulation outcome showed that the proposed model is effective under the conditions: salt & pepper noise, Gaussian noise, rotation, blurring and enhancement. The enhanced GWO based AlexNet model attained maximum detection accuracy of 99.66%, 99.75%, and 98.48% on MICC-F600, MICC-F2000 and GRIP datasets.
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Rajamani, Sripriya, Aaron Bieringer, Stephanie Wallerius, Daniel Jensen, Tamara Winden, and Miriam Halstead Muscoplat. "Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System." Biomedical Informatics Insights 8s2 (January 2016): BII.S40208. http://dx.doi.org/10.4137/bii.s40208.

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Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.
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Zheng, Qingyuan, Zhengyu Jiang, Xinmiao Ni, Song Yang, Panpan Jiao, Jiejun Wu, Lin Xiong, et al. "Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer." International Journal of Molecular Sciences 24, no. 3 (February 1, 2023): 2746. http://dx.doi.org/10.3390/ijms24032746.

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Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TSR associated with better prognosis. Uni- and multivariate Cox regression analyses showed that TSR was independently associated with overall survival (p < 0.001 for all analyses) after adjusting for clinicopathological factors including age, gender, and pathologic stage. TSR was found to be a strong prognostic factor that was not redundant with the existing staging system in different subgroup analyses (p < 0.05 for all analyses). Finally, the expression of six genes (DACH1, DEEND2A, NOTCH4, DTWD1, TAF6L, and MARCHF5) were significantly associated with TSR, revealing possible potential biological relevance. In conclusion, we developed an ML algorithm based on WSIs of MIBC patients to accurately quantify TSR and demonstrated its prognostic validity for MIBC patients in two independent cohorts. This objective quantitative method allows application in clinical practice while reducing the workload of pathologists. Thus, it might be of significant aid in promoting precise pathology services in MIBC.
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Ye, Qing, Ruochen Wang, Chi Zhang, and Yingfeng Cai. "Research on Intelligent Vehicle Path Tracking with Subsystems Based on Multimodel Intelligent Hierarchical Control Theory." Mathematical Problems in Engineering 2021 (June 9, 2021): 1–15. http://dx.doi.org/10.1155/2021/7448517.

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In this paper, a multimodel intelligent hierarchical control (MIHC) algorithm with dual systems is proposed to reduce the performance conflict between a path-tracking motion system and its subsystems during the motion control process of an intelligent vehicle (IV). The working principle of the MIHC algorithm is briefly introduced first, and the dynamic models of IV and the subsystems are constructed. Then, correlation controller models based on MIHC are established. Lastly, the influence of the subsystems on the trajectory tracking of IV is validated through simulations and hardware-in-the-loop test with various condition forms. Results show that the control performance of the automatic steering system has a great influence on the path-tracking accuracy compared with that of the antilock braking system.
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Jue Fu, Ying Zhang,. "Multi-Objective Construction of English Web-Based Independent Learning Based on Mobile Intelligent Information System." Journal of Electrical Systems 20, no. 3s (April 4, 2024): 574–86. http://dx.doi.org/10.52783/jes.1332.

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A Mobile Intelligent Information System (MIIS) represents a transformative tool in the digital age, empowering users with access to dynamic and personalized information anytime, anywhere. Through advanced technologies such as artificial intelligence, machine learning, and natural language processing, MIIS harnesses the power of data to deliver tailored insights and services to users via mobile devices. With its intuitive interface and seamless integration with mobile platforms, MIIS offers a user-friendly experience that adapts to the evolving needs and preferences of users. As mobile technology continues to evolve, MIIS remains at the forefront, revolutionizing the way information is accessed, processed, and utilized in our increasingly interconnected world. This paper presents a multi-objective approach to construct an English web-based independent learning platform leveraging a Mobile Intelligent Information System (MIIS), enhanced by Genetic Integrated Web Optimization (GIWO) with the integration of Ant Bee and Whale Optimization algorithms. The proposed framework aims to optimize multiple objectives, including content relevance, user engagement, and learning effectiveness, in the design and deployment of the learning platform. Through simulated experiments and empirical evaluations, the effectiveness of the GIWO-enhanced MIIS in constructing the web-based learning platform is assessed. Results demonstrate significant improvements in content recommendation accuracy, user satisfaction, and learning outcomes compared to traditional approaches. The integration of Ant Bee and Whale Optimization algorithms further enhances the optimization process, enabling the system to adapt dynamically to evolving user needs and preferences. This study highlights the potential of leveraging MIIS and GIWO algorithms for the multi-objective construction of web-based learning platforms, paving the way for more personalized, efficient, and effective independent learning experiences in English education. The GIWO-enhanced MIIS achieved an average increase of 35% in content relevance, as evidenced by precision and recall scores. Additionally, user satisfaction ratings increased by 40%, indicating higher levels of engagement and perceived usefulness of the platform. Furthermore, learning outcomes improved by 25%, as measured by pre- and post-assessment scores. These simulation results underscore the efficacy of the proposed approach in optimizing multiple objectives for the construction of web-based independent learning platforms in English education.
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Elaskily, Mohamed A., Monagi H. Alkinani, Ahmed Sedik, and Mohamed M. Dessouky. "Deep learning based algorithm (ConvLSTM) for Copy Move Forgery Detection." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 4385–405. http://dx.doi.org/10.3233/jifs-201192.

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Protecting information from manipulation is important challenge in current days. Digital images are one of the most popular information representation. Images could be used in several fields such as military, social media, security purposes, intelligence fields, evidences in courts, and newspapers. Digital image forgeries mean adding unusual patterns to the original images that cause a heterogeneity manner in form of image properties. Copy move forgery is one of the hardest types of image forgeries to be detected. It is happened by duplicating part or section of the image then adding again in the image itself but in another location. Forgery detection algorithms are used in image security when the original content is not available. This paper illustrates a new approach for Copy Move Forgery Detection (CMFD) built basically on deep learning. The proposed model is depending on applying (Convolution Neural Network) CNN in addition to Convolutional Long Short-Term Memory (CovLSTM) networks. This method extracts image features by a sequence number of Convolutions (CNVs) layers, ConvLSTM layers, and pooling layers then matching features and detecting copy move forgery. This model had been applied to four aboveboard available databases: MICC-F220, MICC-F2000, MICC-F600, and SATs-130. Moreover, datasets have been combined to build new datasets for all purposes of generalization testing and coping with an over-fitting problem. In addition, the results of applying ConvLSTM model only have been added to show the differences in performance between using hybrid ConvLSTM and CNN compared with using CNN only. The proposed algorithm, when using number of epoch’s equal 100, gives high accuracy reached to 100% for some datasets with lowest Testing Time (TT) time nearly 1 second for some datasets when compared with the different previous algorithms.
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Amiri, Ehsan, Ahmad Mosallanejad, and Amir Sheikhahmadi. "Copy-Move Forgery Detection Using an Equilibrium Optimization Algorithm (CMFDEOA)." Statistics, Optimization & Information Computing 11, no. 3 (April 20, 2023): 677–84. http://dx.doi.org/10.19139/soic-2310-5070-1511.

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Image forgery detection is a new challenge. One type of image forgery is a copy-move forgery. In this method, part of the image is copied and placed at the most similar point. Given the existing algorithms and processing software, identifying forgery areas is difficult and has created challenges in various applications. The proposed method based on the Equilibrium Optimization Algorithm (EOA) helps image forgery detection by finding forgery areas. The proposed method includes feature detection, image segmentation, and detection of forgery areas using the EOA algorithm. In the first step, the image converts to a grayscale. Then, with the help of a discrete cosine transform (DCT) algorithm, it is taken to the signal domain. With the help of discrete wavelet transform (DWT), its appropriate properties are introduced. In the next step, the image is divided into blocks of equal size. Then the similarity search is performed with the help of an equilibrium optimization algorithm and a suitable proportion function. Copy-move forgery detection using the Equilibrium Optimization Algorithm (CMFDEOA) can find areas of forgery with an accuracy of about 86.21% for the IMD data set and about 83.98% for the MICC-F600 data set.
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Li, Qianmu, Shunmei Meng, Xiaonan Sang, Hanrui Zhang, Shoujin Wang, Ali Kashif Bashir, Keping Yu, and Usman Tariq. "Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing." ACM Transactions on Internet Technology 21, no. 3 (June 9, 2021): 1–33. http://dx.doi.org/10.1145/3408291.

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Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.
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Dissertations / Theses on the topic "Algorithme MIIC"

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Li, Honghao. "Interpretable biological network reconstruction from observational data." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5207.

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Cette thèse porte sur les méthodes basées sur des contraintes. Nous présentons comme exemple l’algorithme PC, pour lequel nous proposons une modification qui garantit la cohérence des ensembles de séparation, utilisés pendant l’étape de reconstruction du squelette pour supprimer les arêtes entre les variables conditionnellement indépendantes, par rapport au graphe final. Elle consiste à itérer l’algorithme d’apprentissage de structure tout en limitant la recherche des ensembles de séparation à ceux qui sont cohérents par rapport au graphe obtenu à la fin de l’itération précédente. La contrainte peut être posée avec une complexité de calcul limitée à l’aide de la décomposition en block-cut tree du squelette du graphe. La modification permet d’augmenter le rappel au prix de la précision des méthodes basées sur des contraintes, tout en conservant une performance globale similaire ou supérieure. Elle améliore également l’interprétabilité et l’explicabilité du modèle graphique obtenu. Nous présentons ensuite la méthode basée sur des contraintes MIIC, récemment développée, qui adopte les idées du cadre du maximum de vraisemblance pour améliorer la robustesse et la performance du graphe obtenu. Nous discutons les caractéristiques et les limites de MIIC, et proposons plusieurs modifications qui mettent l’accent sur l’interprétabilité du graphe obtenu et l’extensibilité de l’algorithme. En particulier, nous mettons en œuvre l’approche itérative pour renforcer la cohérence de l’ensemble de séparation, nous optons pour une règle d’orientation conservatrice et nous utilisons la probabilité d’orientation de MIIC pour étendre la notation des arêtes dans le graphe final afin d’illustrer différentes relations causales. L’algorithme MIIC est appliqué à un ensemble de données d’environ 400 000 dossiers de cancer du sein provenant de la base de données SEER, comme benchmark à grande échelle dans la vie réelle
This thesis is focused on constraint-based methods, one of the basic types of causal structure learning algorithm. We use PC algorithm as a representative, for which we propose a simple and general modification that is applicable to any PC-derived methods. The modification ensures that all separating sets used during the skeleton reconstruction step to remove edges between conditionally independent variables remain consistent with respect to the final graph. It consists in iterating the structure learning algorithm while restricting the search of separating sets to those that are consistent with respect to the graph obtained at the end of the previous iteration. The restriction can be achieved with limited computational complexity with the help of block-cut tree decomposition of the graph skeleton. The enforcement of separating set consistency is found to increase the recall of constraint-based methods at the cost of precision, while keeping similar or better overall performance. It also improves the interpretability and explainability of the obtained graphical model. We then introduce the recently developed constraint-based method MIIC, which adopts ideas from the maximum likelihood framework to improve the robustness and overall performance of the obtained graph. We discuss the characteristics and the limitations of MIIC, and propose several modifications that emphasize the interpretability of the obtained graph and the scalability of the algorithm. In particular, we implement the iterative approach to enforce separating set consistency, and opt for a conservative rule of orientation, and exploit the orientation probability feature of MIIC to extend the edge notation in the final graph to illustrate different causal implications. The MIIC algorithm is applied to a dataset of about 400 000 breast cancer records from the SEER database, as a large-scale real-life benchmark
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Vojtíšek, Jindřich. "Analýza šifrovacích algoritmů ve standardu 802.11." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220648.

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This work deals with wireless standard 802.11, primaly about security algorithms used in them. Further there is made analysis of algorithms WEP, WPA and WPA2. This algorithms are described how coding by them works and for easier understandig are added block schemes of their principles. In practical part is realized algorithms WEP, WPA and WPA2 in program Matlab simulink. Model is complemented by graphs which shows how data changes when comming throught this systems.
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Ye, Fan. "Nouveaux algorithmes numériques pour l’utilisation efficace des architectures multi-cœurs et hétérogènes." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10169/document.

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Cette étude est motivée par les besoins réels de calcul dans la physique des réacteurs. Notre objectif est de concevoir les algorithmes parallèles, y compris en proposant efficaces noyaux algébriques linéaires et méthodes numériques parallèles.Dans un environnement many-cœurs en mémoire partagée tel que le système Intel Many Integrated Core (MIC), la parallélisation efficace d'algorithmes est obtenue en termes de parallélisme des tâches à grain fin et parallélisme de données. Pour la programmation des tâches, deux principales stratégies, le partage du travail et vol de travail ont été étudiées. A des fins de généralité et de réutilisation, nous utilisons des interfaces de programmation parallèle standard, comme OpenMP, Cilk/Cilk+ et TBB. Pour vectoriser les tâches, les outils disponibles incluent Cilk+ array notation, pragmas SIMD, et les fonctions intrinsèques. Nous avons évalué ces techniques et proposé un noyau efficace de multiplication matrice-vecteur dense. Pour faire face à une situation plus complexe, nous proposons d'utiliser le modèle hybride MPI/OpenMP pour la mise en œuvre de noyau multiplication matrice-vecteur creux. Nous avons également conçu un modèle de performance pour modéliser les performances sur MICs et ainsi guider l'optimisation. En ce qui concerne la résolution de systèmes linéaires, nous avons proposé un solveur parallèle évolutif issue de méthodes Monte Carlo. Cette méthode présente un degré de parallélisme abondant, qui s’adapte bien à l'architecture multi-coeurs. Pour répondre à certains des goulots d'étranglement fondamentaux de ce solveur, nous proposons un modèle d'exécution basée sur les tâches qui résout complètement ces problèmes
This study is driven by the real computational needs coming from different fields of reactor physics, such as neutronics or thermal hydraulics, where the eigenvalue problem and resolution of linear system are the key challenges that consume substantial computing resources. In this context, our objective is to design and improve the parallel computing techniques, including proposing efficient linear algebraic kernels and parallel numerical methods. In a shared-memory environment such as the Intel Many Integrated Core (MIC) system, the parallelization of an algorithm is achieved in terms of fine-grained task parallelism and data parallelism. For scheduling the tasks, two main policies, the work-sharing and work-stealing was studied. For the purpose of generality and reusability, we use common parallel programming interfaces, such as OpenMP, Cilk/Cilk+, and TBB. For vectorizing the task, the available tools include Cilk+ array notation, SIMD pragmas, and intrinsic functions. We evaluated these techniques and propose an efficient dense matrix-vector multiplication kernel. In order to tackle a more complicated situation, we propose to use hybrid MPI/OpenMP model for implementing sparse matrix-vector multiplication. We also designed a performance model for characterizing performance issues on MIC and guiding the optimization. As for solving the linear system, we derived a scalable parallel solver from the Monte Carlo method. Such method exhibits inherently abundant parallelism, which is a good fit for many-core architecture. To address some of the fundamental bottlenecks of this solver, we propose a task-based execution model that completely fixes the problems
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Zgheib, Rawad. "Algorithmes adaptatifs d'identification et de reconstruction de processus AR à échantillons manquants." Phd thesis, Université Paris Sud - Paris XI, 2007. http://tel.archives-ouvertes.fr/tel-00273585.

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On souhaite reconstruire en ligne des signaux à échantillons manquants en utilisant une approche paramétrique. On propose alors des algorithmes adaptatifs d'identification et de reconstruction de processus AR à échantillons manquants. On s'intéresse premièrement à l'extension des algorithmes de gradient au cas des signaux à échantillons manquants. On propose alors deux alternatives à un algorithme existant fondées sur deux autres prédicteurs. Les algorithmes proposés convergent vers une estimation non biaisée des paramètres. Or les algorithmes de gradient souffrent d'une faible vitesse de convergence. Pour cela, on s'intéresse à l'extension de l'algorithme MCR au cas des signaux à échantillons manquants. On utilise alors l'algorithme MCR pseudo-linéaire pour l'identification conjointement avec un filtre de Kalman pour une prédiction optimale du signal au sens des moindres carrés. L'algorithme résultant permet une identification non biaisée des paramètres. De plus, il est rapide et bien adapté à l'identification de processus non stationnaires. Néanmoins, souhaitant contrôler la stabilité du filtre identifié, on s'intéresse ensuite à une identification fondée sur une structure en treillis du filtre. Ainsi, on propose une extension de l'algorithme de Burg adaptatif au cas des signaux à échantillons manquants, en utilisant pour la prédiction un filtre de Kalman. La stabilité du modèle ainsi identifié est garantie. De plus, l'algorithme s'adapte rapidement aux variations des paramètres. Finalement, on propose d'utiliser les algorithmes proposés dans un système à transmission non uniforme. On obtient ainsi l'amélioration simultanée du RSB et du débit de transmission moyen.
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Ermolenko, Evgenii. "Algorithm-aided Information Design: Hybrid Design approach on the edge of associative methodologies in AEC." Master's thesis, 2020. http://hdl.handle.net/1822/74793.

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Dissertação de mestrado em European Master in Building Information Modelling
Last three decades have brought colossal progress to design methodologies within the common pursuit toward a seamless fusion between digital and physical worlds and augmenting it with the of computation power and network coverage. For this historically short period, two generations of methodologies and tools have emerged: Additive generation and parametric Associative generation of CAD. Currently, designers worldwide engaged in new forms of design exploration. From this race, two prominent methodologies have developed from Associative Design approach – Object-Oriented Design (OOD) and Algorithm-Aided Design (AAD). The primary research objective is to investigate, examine, and push boundaries between OOD and AAD for new design space determination, where advantages of both design methods are fused to produce a new generation methodology which is called in the present study AID (Algorithm-aided Information Design). The study methodology is structured into two flows. In the first flow, existing CAD methodologies are investigated, and the conceptual framework is extracted based on the state of art analysis, then analysed data is synthesized into the subject proposal. In the second flow, tools and workflows are elaborated and examined on practice to confirm the subject proposal. In compliance, the content of the research consists of two theoretical and practical parts. In the first theoretical part, a literature review is conducted, and assumptions are made to speculate about AID methodology, its tools, possible advantages and drawbacks. Next, case studies are performed according to sequential stages of digital design through the lens of practical AID methodology implementation. Case studies are covering such design aspects as model & documentation generation, design automation, interoperability, manufacturing control, performance analysis and optimization. Ultimately, a set of test projects is developed with the AID methodology applied. After the practical part, research returns to the theory where analytical information is gathered based on the literature review, conceptual framework, and experimental practice reports. In summary, the study synthesizes AID methodology as part of Hybrid Design, which enables creative use of tools and elaborating of agile design systems integrating additive and associative methodologies of Digital Design. In general, the study is based on agile methods and cyclic research development mixed between practice and theory to achieve a comprehensive vision of the subject.
Last three decades have brought colossal progress to design methodologies within the common pursuit toward a seamless fusion between digital and physical worlds and augmenting it with the of computation power and network coverage. For this historically short period, two generations of methodologies and tools have emerged: Additive generation and parametric Associative generation of CAD. Currently, designers worldwide engaged in new forms of design exploration. From this race, two prominent methodologies have developed from Associative Design approach – Object-Oriented Design (OOD) and Algorithm-Aided Design (AAD). The primary research objective is to investigate, examine, and push boundaries between OOD and AAD for new design space determination, where advantages of both design methods are fused to produce a new generation methodology which is called in the present study AID (Algorithm-aided Information Design). The study methodology is structured into two flows. In the first flow, existing CAD methodologies are investigated, and the conceptual framework is extracted based on the state of art analysis, then analysed data is synthesized into the subject proposal. In the second flow, tools and workflows are elaborated and examined on practice to confirm the subject proposal. In compliance, the content of the research consists of two theoretical and practical parts. In the first theoretical part, a literature review is conducted, and assumptions are made to speculate about AID methodology, its tools, possible advantages and drawbacks. Next, case studies are performed according to sequential stages of digital design through the lens of practical AID methodology implementation. Case studies are covering such design aspects as model & documentation generation, design automation, interoperability, manufacturing control, performance analysis and optimization. Ultimately, a set of test projects is developed with the AID methodology applied. After the practical part, research returns to the theory where analytical information is gathered based on the literature review, conceptual framework, and experimental practice reports. In summary, the study synthesizes AID methodology as part of Hybrid Design, which enables creative use of tools and elaborating of agile design systems integrating additive and associative methodologies of Digital Design. In general, the study is based on agile methods and cyclic research development mixed between practice and theory to achieve a comprehensive vision of the subject.
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Books on the topic "Algorithme MIIC"

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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Network growth algorithms. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0008.

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Growth processes are a fundamentally different approach compared to probability-driven exponential models covered in earlier chapters. This chapter studies how growth rules can be designed to mimic processes observed in the real world, and how the process can be mathematically analyzed in order to obtain information about the likely topological properties of the resulting networks. The configuration (stub joining) model is described, including a careful discussion of how bias can be introduced if backtracking is used instead of restarting if stubs join to form a self or double link. The second class of models looked at is preferential attachment. The simplest variants of this are analyzed with a master equation approach, in order to introduce this technique as a way of obtaining analytical information about the expected properties of the generated graphs. Extensive references are provided to the numerous variants and extensions of both of these models.
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Kolm, Petter N., and Lee Maclin. Algorithmic Trading, Optimal Execution, and Dyna Mic Port Folios. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199553433.013.0017.

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Amine, Abdelmalek, Salim Chikhi, Allaoua Chaoui, Mohamed Khireddine Kholladi, and Djamel Eddine Saidouni. Modelling and Implementation of Complex Systems: Proceedings of the 6th International Symposium, MISC 2020, Batna, Algeria, October 24‐26, 2020. Springer, 2020.

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Amine, Abdelmalek, Salim Chikhi, Allaoua Chaoui, and Djamel Eddine Saidouni. Modelling and Implementation of Complex Systems: Proceedings of the 5th International Symposium, MISC 2018, December 16-18, 2018, Laghouat, Algeria. Springer, 2018.

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Amine, Abdelmalek, Salim Chikhi, Allaoua Chaoui, Mohamed Khireddine Kholladi, and Djamel Eddine Saidouni. Modelling and Implementation of Complex Systems: Proceedings of the 4th International Symposium, MISC 2016, Constantine, Algeria, May 7-8, 2016, ... Springer, 2016.

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Diaz-Descalzo, Gregorio, Abdelmalek Amine, Salim Chikhi, Allaoua Chaoui, and Djamel Eddine Saidouni. Modelling and Implementation of Complex Systems: Proceedings of the 7th International Symposium, MISC 2022, Mostaganem, Algeria, October 30‐31 2022. Springer International Publishing AG, 2022.

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Amine, Abdelmalek, Salim Chikhi, Allaoua Chaoui, Mohamed Khireddine Kholladi, and Djamel Eddine Saidouni. Modelling and Implementation of Complex Systems: Proceedings of the 4th International Symposium, MISC 2016, Constantine, Algeria, May 7-8, 2016, Constantine, Algeria. Springer London, Limited, 2016.

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Goleman, Travis. Machine Learning and Artificial Intelligence 2 Manuscripts In 1: The Essential Guide to Understand Artificial Intelligence, Machine Learning, Mimic Human Behavior, NLP Algorithms and Deep Learning. Independently Published, 2019.

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Goleman, Travis. Artificial Intelligence for Beginners: All You Have to Know about the Potential of AI in the Future, Techniques to Mimic Human Behavior, Deep Learning and NLP Algorithms. Independently Published, 2019.

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Shroff, Gautam. The Intelligent Web. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199646715.001.0001.

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As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.
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Book chapters on the topic "Algorithme MIIC"

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Liu, Yang, and Liang Deng. "Acceleration of CFD Engineering Software on GPU and MIC." In Algorithms and Architectures for Parallel Processing, 835–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27161-3_77.

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Ju, Tao, Zhengdong Zhu, Yinfeng Wang, Liang Li, and Xiaoshe Dong. "Thread Mapping and Parallel Optimization for MIC Heterogeneous Parallel Systems." In Algorithms and Architectures for Parallel Processing, 300–311. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11194-0_23.

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Huang, Kun, and Yifeng Chen. "Improving Performance of Floating Point Division on GPU and MIC." In Algorithms and Architectures for Parallel Processing, 691–703. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_48.

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Liang, Weihao, Hong An, Feng Li, and Yichao Cheng. "Optimization of Binomial Option Pricing on Intel MIC Heterogeneous System." In Algorithms and Architectures for Parallel Processing, 17–29. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27137-8_2.

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Chen, Cheng, Yunfei Du, Zhen Xu, and Canqun Yang. "FT-Offload: A Scalable Fault-Tolerance Programing Model on MIC Cluster." In Algorithms and Architectures for Parallel Processing, 3–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27140-8_1.

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Szustak, Lukasz, Kamil Halbiniak, Adam Kulawik, Roman Wyrzykowski, Piotr Uminski, and Marcin Sasinowski. "Using hStreams Programming Library for Accelerating a Real-Life Application on Intel MIC." In Algorithms and Architectures for Parallel Processing, 373–82. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49956-7_30.

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Wang, Ruping, Hui Li, Mei Chen, Zhenyu Dai, and Ming Zhu. "MIC-KMeans: A Maximum Information Coefficient Based High-Dimensional Clustering Algorithm." In Advances in Intelligent Systems and Computing, 208–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91189-2_21.

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Lirkov, Ivan, Yavor Vutov, Marcin Paprzycki, and Maria Ganzha. "Parallel Performance Evaluation of MIC(0) Preconditioning Algorithm for Voxel μFE Simulation." In Parallel Processing and Applied Mathematics, 135–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14403-5_15.

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Sun, Wei, Shaoxiong Ji, Erik Cambria, and Pekka Marttinen. "Multitask Recalibrated Aggregation Network for Medical Code Prediction." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 367–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86514-6_23.

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AbstractMedical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.
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Arslantas, Mustafa Kemal, Tunc Asuroglu, Reyhan Arslantas, Emin Pashazade, Pelin Corman Dincer, Gulbin Tore Altun, and Alper Kararmaz. "Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU." In Communications in Computer and Information Science, 3–16. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_1.

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AbstractSerum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients, as suggested by The Surviving Sepsis Campaign, early lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and trend analysis are gaining importance to overcome these issues. In this context, we aimed to investigate if a machine learning approach can predict lactate trends from non-invasive parameters of patients with sepsis. This retrospective study analyzed adult sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset. Inclusion criteria were two or more lactate tests within 6 h of diagnosis, an ICU stay of at least 24 h, and a change of ≥1 mmol/liter in lactate level. Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers were evaluated for lactate trend prediction. LMT algorithm outperformed other classifiers (AUC = 0.803; AUPRC = 0.921). J48 decision tree performed worse than the other methods when predicting constant trend. LMT algorithm with four features (heart rate, oxygen saturation, initial lactate, and time interval variables) achieved 0.80 in terms of AUC (AUPRC = 0.921). We can say that machine learning models that employ logistic regression architectures, i.e., LMT algorithm achieved good results in lactate trend prediction tasks, and it can be effectively used to assess the state of the patient, whether it is stable or improving.
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Conference papers on the topic "Algorithme MIIC"

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Lu, Dau-Tsuong, Ramamohan Paturi, Sadik Esener, and Sing H. Lee. "Parallel algorithms with irregular interconnections and optical technology." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.mii3.

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We consider the implementation of fast parallel algorithms. We make the case that irregular interconnections result in faster parallel algorithms; consequently, optical technologies that can support such randomlike interconnections are preferable. We demonstrate this by selecting a problem which has very fast parallel algorithms when irregular interconnections are used, and by suggesting certain optical technologies that can support such irregular interconnections for implementing these fast parallel algorithms. (On the other hand, it is well known that any technology that can only support regular and planar interconnections cannot achieve the same speedup.) The problem is that of approximately halving a sequence of numbers. This problem comes up as a natural subproblem in the optimal sorting algorithm of Ajtai, Komlos, and Szemeredi.1 We use probabilistic means to construct the interconnections necessary for the approximate halver problem and certify their properties by computing their spectrum. We also show that the interconnection pattern underlies several other fast parallel algorithms such as sorting, selection, load balancing, distributed consensus, and error-correcting codes. We then suggest that POEM (programmable optoelectronic multiprocessor) technology is capable of implementing such randomlike interconnections.
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Ko, Ching Yun, Rui Lin, Shu Li, and Ngai Wong. "MiSC: Mixed Strategies Crowdsourcing." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/193.

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Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation. Recently, another cohort of models regard crowdsourced annotations as incomplete tensors and recover unfilled labels by tensor completion. However, mixed strategies of the two methodologies have never been comprehensively investigated, leaving them as rather independent approaches. In this work, we propose MiSC ( Mixed Strategies Crowdsourcing), a versatile framework integrating arbitrary conventional crowdsourcing and tensor completion techniques. In particular, we propose a novel iterative Tucker label aggregation algorithm that outperforms state-of-the-art methods in extensive experiments.
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Kumar, Shivesh, Marc Simnofske, Bertold Bongardt, Andreas Müller, and Frank Kirchner. "Integrating Mimic Joints into Dynamics Algorithms." In the Advances in Robotics. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3132446.3134891.

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Navaz, K., S. Yazhinian, N. Muthuvairavan Pillai, Neelamegam Devarasu, and M. Sankar. "MXDD Scheduling Algorithm for MIBC Switches." In 2023 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2023. http://dx.doi.org/10.1109/icscan58655.2023.10395457.

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Tuncel, Mehmet, Ahmet Duran, M. Serdar Celebi, Bora Akaydin, and Figen O. Topkaya. "A comparison of SuperLU solvers on the intel MIC architecture." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965394.

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Ju, Tao, Xiaoshe Dong, Endong Wang, Liang Li, and Zhengdong Zhu. "Parallel Optimization Strategies for MIC Heterogeneous Parallel Systems." In 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2014. http://dx.doi.org/10.1109/paap.2014.39.

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Neupane, Aadesh, and Michael Goodrich. "Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/73.

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Algorithms used in networking, operation research and optimization can be created using bio-inspired swarm behaviors, but it is difficult to mimic swarm behaviors that generalize through diverse environments. State-machine-based artificial collective behaviors evolved by standard Grammatical Evolution (GE) provide promise for general swarm behaviors but may not scale to large problems. This paper introduces an algorithm that evolves problem-specific swarm behaviors by combining multi-agent grammatical evolution and Behavior Trees (BTs). We present a BT-based BNF grammar, supported by different fitness function types, which overcomes some of the limitations in using GEs to evolve swarm behavior. Given human-provided, problem-specific fitness-functions, the learned BT programs encode individual agent behaviors that produce desired swarm behaviors. We empirically verify the algorithm's effectiveness on three different problems: single-source foraging, collective transport, and nest maintenance. Agent diversity is key for the evolved behaviors to outperform hand-coded solutions in each task.
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Junling Li, Bohu Liang, and Xiaodong Su. "Research on ECG signal filtering algorithm based on the fusion of multiple algorithms." In 2012 International Conference on Measurement, Information and Control (MIC). IEEE, 2012. http://dx.doi.org/10.1109/mic.2012.6273273.

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Changfei, Zhou, Lu Kai, Chi Wanqing, Wang Xiaoping, and Xiong Zhenhai. "MIC Acceleration of Saliency Detection Algorithm." In 1st International Workshop on Cloud Computing and Information Security. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/ccis-13.2013.73.

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Wu, Zhaoqi, and Jin Wei. "Heterogeneous Executors Scheduling Algorithm for Mimic Defense Systems." In 2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET). IEEE, 2019. http://dx.doi.org/10.1109/ccet48361.2019.8989231.

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