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1000 tulosta hakusanalla Angeline Bauer
AI-Powered COVID-19 Prediction
Angeline Prasanna Gopalan; Jinu Paulson Siluvairathinam
Lap Lambert Academic Publishing
2025
pokkari
Die Coronavirus-Krankheit - 2019 (COVID-19) ist eine Viruserkrankung, die durch das Schwere Akute Respiratorische Syndrom (SARS) verursacht wird und zur Gruppe der Corona-Viridae geh rt. Sie wurde erstmals im Dezember 2019 in Wuhan, China, entdeckt und im Februar 2020 von der Weltgesundheitsorganisation zu einer globalen Bedrohung erkl rt . Diagnose und Behandlung sind von entscheidender Bedeutung, und bildgebende Verfahren wie Computertomografie (CT) und R ntgenstrahlen werden eingesetzt, um die Auswirkungen des Virus auf den K rper zu berwachen. Deep-Learning-Modelle wie Convolutional Neural Networks (CNN) wurden f r die COVID-19-Erkennung erforscht. Diese Modelle waren jedoch nicht in der Lage, mehrdeutige Grenzen und unsichere Kanten zu erkennen, was zu einer schlechten Segmentierungseffizienz f hrt. Au erdem kam es aufgrund hnlicher Merkmale in gesundem und COVID-19-Gewebe zu Fehlklassifizierungen verschiedener Schweregrade . Diese Forschung zielt darauf ab, diese Herausforderungen zu bew ltigen und ein neues Modell f r eine genauere Diagnose bereitzustellen.
Le Coronavirus - 2019 (COVID-19) est une maladie virale caus e par le syndrome respiratoire aigu s v re (SRAS) et fait partie du groupe des Corona viridae. Elle a t d tect e pour la premi re fois Wuhan, en Chine, en d cembre 2019 et a t d clar e menace mondiale par l'Organisation mondiale de la sant en f vrier 2020. Le diagnostic et le traitement sont cruciaux, et des techniques d'imagerie telles que la tomodensitom trie (CT) et les rayons X sont utilis es pour surveiller les effets du virus sur l'organisme. Des mod les d'apprentissage profond, tels que les r seaux neuronaux convolutifs (CNN), ont t tudi s pour la d tection de COVID-19. Toutefois, ces mod les ne parviennent pas distinguer les limites ambigu s et les bords incertains, ce qui nuit l'efficacit de la segmentation. En outre, la classification erron e des diff rents niveaux de gravit est due la similitude des caract ristiques des tissus sains et des tissus COVID-19. Cette recherche vise relever ces d fis et fournir un nouveau mod le pour un diagnostic plus pr cis.
La malattia da Coronavirus - 2019 (COVID-19) una malattia virale causata dalla sindrome respiratoria acuta grave (SARS) e fa parte del gruppo Corona viridae. stata rilevata per la prima volta a Wuhan, in Cina, nel dicembre 2019 ed stata dichiarata una minaccia globale dall'Organizzazione Mondiale della Sanit nel febbraio 2020. La diagnosi e il trattamento sono fondamentali e le tecniche di imaging come la tomografia computerizzata (TC) e i raggi X sono utilizzati per monitorare gli effetti del virus sul corpo. Modelli di apprendimento profondo, come le reti neurali convoluzionali (CNN), sono stati esplorati per il rilevamento del COVID-19. Tuttavia, questi modelli nonsono riusciti a distinguere le ambiguit del virus. Tuttavia, questi modelli non sono riusciti a distinguere i confini ambigui e i bordi incerti, il che tende a una scarsa efficienza di segmentazione. Inoltre, l'errata classificazione dei diversi livelli di gravit si verificata a causa delle caratteristiche simili nei tessuti sani e in quelli COVID-19. Questa ricerca si propone di affrontare queste sfide e di fornire un nuovo modello per una diagnosi pi accurata.
Covid-19
Angeline Prasanna Gopalan; Jinu Paulson Siluvairathinam
Wydawnictwo Nasza Wiedza
2026
pokkari
Choroba koronawirusowa - 2019 (COVID-19) to choroba wirusowa wywolywana przez zesp l ciężkiej ostrej niewydolności oddechowej (SARS) i należy do grupy Corona viridae. Po raz pierwszy wykryto ją w Wuhan w Chinach w grudniu 2019 r., a w lutym 2020 r. Światowa Organizacja Zdrowia oglosila ją globalnym zagrożeniem . Diagnostyka i leczenie mają kluczowe znaczenie, a techniki obrazowania, takie jak tomografia komputerowa (CT) i promieniowanie rentgenowskie, są wykorzystywane do monitorowania wplywu wirusa na organizm. Modele glębokiego uczenia, takie jak konwolucyjne sieci neuronowe (CNN), zostaly zbadane pod kątem wykrywania COVID-19. Modele te nie rozr żnialy jednak niejednoznacznych granic i niepewnych krawędzi, co prowadzilo do niskiej skuteczności segmentacji. Ponadto blędna klasyfikacja r żnych poziom w nasilenia wystąpila z powodu podobnych cech w tkankach zdrowych i COVID-19. Niniejsze badania mają na celu sprostanie tym wyzwaniom i zapewnienie nowego modelu dla dokladniejszej diagnozy.
Montessori is the oldest and most widespread alternative educational system available today. In the United States alone, Montessori is implemented in thousands of schools, including over 600 public ones. Its main claim is that the pedagogy is closely aligned with developmental science and that given the appropriate environment, infants and children develop themselves. Yet studies of the outcomes of this form of education have thus far been scattered, with no intensive qualitative review to inform parents, teachers, administrators, researchers, and school districts of what we know about Montessori's outcomes. The Montessori Difference examines and synthesizes the existing body of research on the outcomes of Montessori education to consider the efficacy of a Montessori education. It introduces the main tenets of Montessori education; reviews academic outcomes in literacy, math, and social-emotional outcomes like executive function and wellbeing; and offers a primer on how to interpret research, including meta-analyses, the pros and cons of different research designs to study school outcomes, and effect sizes. In doing so, it dispels some of the myths of Montessori and considers the challenges and opportunities inherent in the broader implementation of Montessori schooling. Pointing out both weaknesses and strengths in the existing literature, Angeline Stoll Lillard weaves together evidence from the highest quality studies to conclude when and how the research suggests this century-old way of educating makes a difference in the outcomes of children.
Mommy Daddy and He: For First Baby Boy
Taylor Angeline; Ozo
Silver Cord Records, Incorporated
2017
nidottu
Mommy and Daddy and He is a fun nursery rhyme for first baby boys. It focuses on the Mye family's love for one another as seen through the eyes of the son, He Mye. This book can be read to a boy three years old and up. As he grows he can then read it to his parents. The tongue twisting rhyming will help improve his reading skills, and the comforting words will make him feel safe and loved. The more loved and safer he feels the better his emotional and physical health will be throughout his life. Families with love for each other are the happiest Each scene includes a game where he can find The Family of Three Hearts.
God created "Multi-Colors" of people to stand together when times are rough. Jesus wants us to love even when its hard to love a person of color. Search deep within your heart and see how you would like to be treated.
God created "Multi-Colors" of people to stand together when times are rough. Jesus wants us to love even when its hard to love a person of color. Search deep within your heart and see how you would like to be treated.
Words to Live By: A coloring book to inspire, encourage and heal...
Marie Angeline Parker
Createspace Independent Publishing Platform
2017
nidottu
A collection of 16 positive words to color and personalize. Each page was hand-drawn and chosen specifically for this coloring book. The artist, Marie, is a Richmond based artist-entrepreneur. It is her dream to live off of the art that she creates or helps other people create.
Third Wave Pentecostalism in the Philippines
Lora Angeline Embudo Timenia; Robert P Menzies
Wipf Stock Publishers
2021
pokkari
Third Wave Pentecostalism in the Philippines
Lora Angeline Embudo Timenia; Robert P Menzies
Wipf Stock Publishers
2021
sidottu
A collection of all SEA-WITCH materials into a colossal mythological document. A search for a body within the greater body of all. A search for no body. 400 pages of total indulgence by Never Angeline N rth.