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298 tulosta hakusanalla Adarsh Sharma

La réalité augmentée et son utilisation en dentisterie pédiatrique

La réalité augmentée et son utilisation en dentisterie pédiatrique

Adharsh Km; Navneet Agrawal; Gaurav Mishra

Editions Notre Savoir
2024
pokkari
La r alit augment e (RA) est une technologie qui superpose au monde r el des images, des sons et d'autres donn es sensorielles g n r es par ordinateur, am liorant ainsi la perception de la r alit par l'utilisateur. En dentisterie p diatrique, la r alit augment e appara t comme une technique de distraction audiovisuelle efficace pour att nuer l'anxi t et l'inconfort des jeunes patients pendant les proc dures dentaires. L'utilisation de la r alit augment e comme technique de distraction audiovisuelle en dentisterie p diatrique offre une approche prometteuse pour am liorer l'exp rience dentaire globale des jeunes patients. En r duisant l'anxi t , en g rant la douleur et en am liorant la coop ration, la r alit augment e peut conduire de meilleurs r sultats dentaires et favoriser une attitude positive l' gard des soins dentaires chez les enfants. Au fur et mesure que la technologie progresse, le potentiel des applications de la RA en dentisterie p diatrique est susceptible de s' tendre, ce qui en fait un outil inestimable pour les professionnels des soins dentaires.
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La realtà aumentata e il suo utilizzo in odontoiatria pediatrica

La realtà aumentata e il suo utilizzo in odontoiatria pediatrica

Adharsh Km; Navneet Agrawal; Gaurav Mishra

Edizioni Sapienza
2024
pokkari
La realt aumentata (AR) una tecnologia che sovrappone immagini, suoni e altri input sensoriali generati dal computer al mondo reale, migliorando la percezione della realt da parte dell'utente. In odontoiatria pediatrica, la AR sta emergendo come un'efficace tecnica di distrazione audiovisiva per alleviare l'ansia e il disagio dei piccoli pazienti durante le procedure odontoiatriche. L'uso della realt aumentata come tecnica di distrazione audiovisiva in odontoiatria pediatrica offre un approccio promettente per migliorare l'esperienza odontoiatrica complessiva dei piccoli pazienti. Riducendo l'ansia, gestendo il dolore e migliorando la cooperazione, la AR pu portare a risultati odontoiatrici migliori e favorire un atteggiamento positivo nei confronti delle cure dentistiche nei bambini. Con il progredire della tecnologia, il potenziale delle applicazioni della AR in odontoiatria pediatrica destinato a crescere, diventando uno strumento prezioso per i professionisti del settore.
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Realidade aumentada e seu uso em odontopediatria

Realidade aumentada e seu uso em odontopediatria

Adharsh Km; Navneet Agrawal; Gaurav Mishra

Edicoes Nosso Conhecimento
2024
pokkari
A Realidade Aumentada (RA) uma tecnologia que sobrep e imagens, sons e outros est mulos sensoriais gerados por computador ao mundo real, melhorando a perce o da realidade por parte do utilizador. Em odontopediatria, a RA est a emergir como uma t cnica de distra o audiovisual eficaz para aliviar a ansiedade e o desconforto dos pacientes jovens durante os procedimentos dent rios. A utiliza o da realidade aumentada como t cnica de distra o audiovisual em odontopediatria oferece uma abordagem promissora para melhorar a experi ncia dent ria global dos pacientes jovens. Ao reduzir a ansiedade, gerir a dor e melhorar a coopera o, a RA pode conduzir a melhores resultados dent rios e promover uma atitude positiva em rela o aos cuidados dent rios nas crian as. medida que a tecnologia avan a, prov vel que o potencial das aplica es da RA em odontopediatria se expanda, tornando-a uma ferramenta inestim vel para os profissionais de medicina dent ria.
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Augmented Reality and Its Use in Pediatric Dentistry

Augmented Reality and Its Use in Pediatric Dentistry

Adharsh Km; Navneet Agrawal; Gaurav Mishra

Lap Lambert Academic Publishing
2024
pokkari
Augmented Reality (AR) is a technology that superimposes computer-generated images, sounds, and other sensory inputs onto the real world, enhancing the user's perception of reality. In pediatric dentistry, AR is emerging as an effective audio-visual distraction technique to alleviate anxiety and discomfort in young patients during dental procedures. The use of augmented reality as an audio-visual distraction technique in pediatric dentistry offers a promising approach to enhance the overall dental experience for young patients. By reducing anxiety, managing pain, and improving cooperation, AR can lead to better dental outcomes and foster a positive attitude towards dental care in children. As technology advances, the potential for AR applications in pediatric dentistry is likely to expand, making it an invaluable tool for dental professionals.
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Immediate Implant Placement

Immediate Implant Placement

Aadarsh Dhan

UNKNOWN
2024
nidottu
Restoring teeth to their ideal state regarding form, comfort, appearance, function, speech & general health is goal of modern dentistry. This can be accomplished by treating dental caries or by replacing several missing teeth, fixed, removable, or implant-supported prostheses being most common options.1 Patients who are completely or partially edentulous can now have restorations done on them thanks to placement of endosseous implants. Word "immediate" appears in the implant literature almost every day, albeit it's frequently used ambiguously. When the phrase was first used, it referred to the procedure of placing implant during extracting tooth. It is currently used for refering to the immediate insertion of a provisional restoration into a socket or onto a healed bone complex on an implant that has just been positioned. The phrase is also used when a provisional restoration is positioned on an implant that is placed right away and the restoration needs to accept functional occlusal loads right away.
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The Balloon Boy

The Balloon Boy

Akarsh Mewara

Clever Fox Publishing
2023
pokkari
I am Josh, well Josh Jones. I basically live in a planet Alister-GBR where every person looks like a balloon. My life has many different stories which almost get me killed but I luckily survive out of them. There are problems coming at me non stop so stay with me on this journey of comedy, adventure, horror and many more...
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THE HEAVENLY LAND AND THE LAND OF THE RISING SUN Historical Linkages, Security Cooperation and Strategic Partnership

THE HEAVENLY LAND AND THE LAND OF THE RISING SUN Historical Linkages, Security Cooperation and Strategic Partnership

Adarsha Verma

K W Publishers Pvt Ltd
2020
sidottu
Security relations between India and Japan hold great potential to shape the future security architecture of the Indo-Pacific region. This book delves into this aspect holistically tracing the linkages between the two countries with advent of Buddhism into Japan from India, through China and Korea. Geography and strategic factors shaping the security of Japan have been evaluated and issues of defence cooperation, maritime security, cooperation in UN Peace Keeping Operations and strategic partnership between Indian and Japan have been deliberated. Set in both, a bilateral as well as a regional context, the security dynamics between the two countries have been analysed to arrive at pragmatic recommendations that must be implemented for an enhanced relationship in the security realm. Quantitatively assessing India-Japan security cooperation, the book carries out a Strength, Weakness, Opportunity and Threat (SWOT) analysis to arrive at the strategies for enhancement of such cooperation.A must read for strategists, defence personnel and all scholars of East Asian affairs.India-Japan cultural and security relations is a subject that has always been intriguing considering the potential it holds for the future security architecture in the Indo-Pacific region. The opportunity to do a research fellowship at the Manohar Parrikar Institute for Defence Studies and Analyses (Manohar Parrikar IDSA) came as a blessing and gave direction to my interest of writing on the subject.
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Distribution Management

Distribution Management

Aadarsh Kumar

Discovery Publishing House (India)
2024
nidottu
In the multifaceted web of global supply chains, distribution management stands as a cornerstone, organizing the seamless movement of goods from manufacturers to retailers and, ultimately, to end consumers. This multifaceted discipline involves the coordination and oversight of various critical activities, including transportation, warehousing, inventory management, and order fulfillment. Distribution management is pivotal for optimizing the logistics process. Through strategic planning and meticulous execution, distribution managers can mitigate transportation costs, minimize expenses associated with holding inventory, and enhance overall operational efficiency. Businesses that prioritize and invest in effective distribution management not only streamline their distribution processes but also elevate customer service standards. This, in turn, contributes to an enhanced market position and a greater likelihood of success in an environment where adaptability and efficiency are paramount. Distribution management is not merely a logistical function but a strategic imperative for businesses aspiring to thrive in the complex landscape of modern supply chains. This book "Distribution Management" is to provide a comprehensive and authoritative resource for professionals, scholars, and students in the field of distribution management. This book seeks to distill the key principles, strategies, and best practices essential for effective distribution in diverse industries. The primary objective is to offer a holistic understanding of distribution management, encompassing the entire spectrum from supply chain logistics to retail operations.
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Natural Language Processing Recipes

Natural Language Processing Recipes

Akshay Kulkarni; Adarsha Shivananda

APRESS
2021
nidottu
Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP. The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks. After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.What You Will LearnKnow the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and moreImplement text pre-processing and feature engineering in NLP, including advanced methods of feature engineeringUnderstand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learningWho This Book Is ForData scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercises
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Natural Language Processing Projects

Natural Language Processing Projects

Akshay Kulkarni; Adarsha Shivananda; Anoosh Kulkarni

APRESS
2021
nidottu
Leverage machine learning and deep learning techniques to build fully-fledged natural language processing (NLP) projects. Projects throughout this book grow in complexity and showcase methodologies, optimizing tips, and tricks to solve various business problems. You will use modern Python libraries and algorithms to build end-to-end NLP projects. The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentences using contextual embedding, summarizing huge documents using recurrent neural networks (RNN), automatic word suggestion using long short-term memory networks (LSTM), and how to build a chatbot using transfer learning. It concludes with an exploration of next-generation AI and algorithms in the research space. By the end of this book, you will have the knowledge needed to solve various business problems using NLP techniques.What You Will LearnImplement full-fledged intelligent NLP applications with PythonTranslate real-world business problem on text data with NLP techniquesLeverage machine learning and deep learning techniques to perform smart language processingGain hands-on experience implementing end-to-end search engine information retrieval, text summarization, chatbots, text generation, document clustering and product classification,and more Who This Book Is ForData scientists, machine learning engineers, and deep learning professionals looking to build natural language applications using Python
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Applied Recommender Systems with Python

Applied Recommender Systems with Python

Akshay Kulkarni; Adarsha Shivananda; Anoosh Kulkarni; V Adithya Krishnan

APRESS
2022
nidottu
This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
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Time Series Algorithms Recipes

Time Series Algorithms Recipes

Akshay R Kulkarni; Adarsha Shivananda; Anoosh Kulkarni; V Adithya Krishnan

APRESS
2022
nidottu
This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
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Introduction to Prescriptive AI

Introduction to Prescriptive AI

Akshay Kulkarni; Adarsha Shivananda; Avinash Manure

APRESS
2023
nidottu
Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.The book kicks off with an introduction to decision intelligence and provides insight into prescriptive AI and how it can be woven into various business strategies and frameworks. You'll then be introduced to different decision intelligence methodologies and how to implement them, along with advantages and limitations of each. Digging deeper, the authors then walk you through how to perform simulations and interpret the results. A full chapter is devoted to embedding decision intelligence processes and outcomes into your business workflow using various applications. The book concludes by exploring different cognitive biases humans are prone to, and how those biasescan be eliminated by combining machine and human intelligence.Upon completing this book, you will understand prescriptive AI, tools, and techniques and will be ready to incorporate them into your business workflow.What You Will LearnImplement full-fledged decision intelligence applications using PythonLeverage the tools, techniques, and methodologies for prescriptive AIUnderstand how prescriptive AI can be used in different domains through practical examplesInterpret results and integrate them into your decision makingWho This Book Is ForData Scientists and Machine Learning Engineers, as well as business professionals who want to understand how AI-driven decision intelligence can help grow their business.
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Applied Generative AI for Beginners

Applied Generative AI for Beginners

Akshay Kulkarni; Adarsha Shivananda; Anoosh Kulkarni; Dilip Gudivada

APRESS
2023
nidottu
This book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI. Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You’ll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains. Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights. What You Will Learn Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google BardImplement large language models using Sklearn, complete with code examples and best practices for real-world applicationLearn how to integrate LLM’s in enterprises, including aspects like LLMOps and technology stack selectionUnderstand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights Who This Book Is For Data scientists, AI practitioners, Researchers and software engineers interested in generative AI and LLMs.
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Application of Deep Learning in Object Detection

Application of Deep Learning in Object Detection

Amit Kumar Sinha; Adarsha Ruwali; Abhilash Jha

LAP Lambert Academic Publishing
2019
pokkari
The journey of writing this piece begins during our graduation period. After a long search we selected 'Application of Deep Learning In Object Detection'. We read all most 40 papers and came to a conclusion that we should go for combining our work for those who really are interested in this area. We comprehended all most all piece of our work and have included in simple and easy language so that reader can easy understand and get it.
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