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001484465 0247_ $$a10.1007/978-981-99-4558-0$$2doi
001484465 035__ $$aSP(OCoLC)1411308134
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001484465 050_4 $$aHD45$$b.T46 2024
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001484465 08204 $$a658.0028563$$223/eng/20231208
001484465 1001_ $$aTeoh, Teik Toe.
001484465 24510 $$aArtificial intelligence in business management /$$cTeik Toe Teoh, Yu Jin Goh.
001484465 264_1 $$aSingapore :$$bSpringer,$$c2023.
001484465 300__ $$a1 online resource (385 p.).
001484465 336__ $$atext$$btxt$$2rdacontent
001484465 337__ $$acomputer$$bc$$2rdamedia
001484465 338__ $$aonline resource$$bcr$$2rdacarrier
001484465 4900_ $$aMachine Learning: Foundations, Methodologies, and Applications
001484465 500__ $$a10.3.2 Sales Assistant Chatbot
001484465 5050_ $$aIntro -- Preface -- Acknowledgments -- Contents -- Part I Artificial Intelligence Algorithms -- 1 Introduction to Artificial Intelligence -- 1.1 Introduction -- 1.2 History of Artificial Intelligence -- 1.3 Types of Artificial Intelligence Algorithms -- 1.4 Organization of the Book -- References -- 2 Regression -- 2.1 Linear Regression -- 2.2 Decision Tree Regression -- 2.3 Random Forests -- 2.4 Neural Network -- 2.5 Improving Regression Performance -- 2.5.1 Boxplot -- 2.5.2 Remove Outlier -- 2.5.3 Remove NA -- 2.5.4 Feature Importance -- Exercises -- References -- 3 Classification
001484465 5058_ $$a3.1 Logistic Regression -- 3.2 Decision Tree and Random Forest -- 3.3 Neural Network -- 3.4 Support Vector Machines -- 3.4.1 Important Hyperparameters -- 3.5 Naive Bayes -- 3.6 Improving Classification Performance -- Exercises -- References -- 4 Clustering -- 4.1 Introduction to Clustering -- 4.2 K-means -- 4.3 The Elbow Method -- Exercises -- References -- 5 Time Series -- 5.1 Introduction to Time Series -- 5.2 Stationarity -- 5.3 Level, Trend, and Seasonality -- 5.4 Exponential Smoothing -- 5.4.1 Simple Exponential Smoothing -- 5.4.2 Double Exponential Smoothing (Holt's Exponential Smoothing)
001484465 5058_ $$a5.4.3 Triple Exponential Smoothing (Holt-Winters Exponential Smoothing) -- 5.5 Moving Average Smoothing -- 5.6 Autoregression -- 5.7 Moving Average Process -- 5.8 SARIMA -- 5.9 ARCH/GARCH -- Exercises -- References -- 6 Convolutional Neural Networks -- 6.1 The Convolution Operation -- 6.2 Pooling -- 6.3 Flattening -- 6.4 Building a CNN -- 6.5 CNN Architectures -- 6.5.1 VGG16 -- 6.5.2 InceptionNet -- 6.5.3 ResNet -- 6.6 Finetuning -- 6.7 Other Tasks That Use CNNs -- 6.7.1 Object Detection -- 6.7.2 Semantic Segmentation -- Exercises -- References -- 7 Text Mining -- 7.1 Preparing the Data
001484465 5058_ $$a7.2 Texts for Classification -- 7.3 Vectorize -- 7.4 TF-IDF -- 7.5 Web Scraping -- 7.6 Tokenization -- 7.7 Part of Speech Tagging -- 7.8 Stemming and Lemmatization -- Exercises -- Reference -- 8 Chatbot, Speech, and NLP -- 8.1 Speech to Text -- 8.2 Preparing the Data for Chatbot -- 8.2.1 Download the Data -- 8.2.2 Reading the Data from the Files -- 8.2.3 Preparing Data for Seq2Seq Model -- 8.3 Defining the Encoder-Decoder Model -- 8.4 Training the Model -- 8.5 Defining Inference Models -- 8.6 Talking with Our Chatbot -- Exercises -- References
001484465 5058_ $$aPart II Applications of Artificial Intelligence in Business Management -- 9 AI in Human Resource Management -- 9.1 Introduction to Human Resource Management -- 9.2 Artificial Intelligence in Human Resources -- 9.3 Applications of AI in Human Resources -- 9.3.1 Salary Prediction -- 9.3.2 Recruitment -- 9.3.3 Course Recommendation -- 9.3.4 Employee Attrition Prediction -- Exercises -- References -- 10 AI in Sales -- 10.1 Introduction to Sales -- 10.1.1 The Sales Cycle -- 10.2 Artificial Intelligence in Sales -- 10.3 Applications of AI in Sales -- 10.3.1 Lead Scoring
001484465 506__ $$aAccess limited to authorized users.
001484465 520__ $$aArtificial intelligence (AI) is rapidly gaining significance in the business world. With more and more organizations adopt AI technologies, there is a growing demand for business leaders, managers, and practitioners who can harness AIs potential to improve operations, increase efficiency, and drive innovation. This book aims to help management professionals exploit the predictive powers of AI and demonstrate to AI practitioners how to apply their expertise in fundamental business operations. It showcases how AI technology innovations can enhance various aspects of business management, such as business strategy, finance, and marketing. Readers interested in AI for business management will find several topics of particular interest, including how AI can improve decision-making in business strategy, streamline operational processes, and enhance customer satisfaction. As AI becomes an increasingly important tool in the business world, this book offers valuable insights into how it can be applied to various industries and business settings. Through this book, readers will gain a better understanding of how AI can be applied to improve business management practices and practical guidance on how to implement AI projects in a business context. This book also provides practical guides on how to implement AI projects in a business context using Python programming. By reading this book, readers will be better equipped to make informed decisions about how to leverage AI for business success.
001484465 650_6 $$aGestion d'entreprise$$xInnovations.
001484465 650_0 $$aIndustrial management$$xTechnological innovations.$$zAsia$$0(DLC)sh2008123404
001484465 655_0 $$aElectronic books.
001484465 7001_ $$aGoh, Yu Jin.
001484465 77608 $$iPrint version:$$aTeoh, Teik Toe$$tArtificial Intelligence in Business Management$$dSingapore : Springer,c2024
001484465 852__ $$bebk
001484465 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-4558-0$$zOnline Access$$91397441.1
001484465 909CO $$ooai:library.usi.edu:1484465$$pGLOBAL_SET
001484465 980__ $$aBIB
001484465 980__ $$aEBOOK
001484465 982__ $$aEbook
001484465 983__ $$aOnline
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