Hepatitis C Classification And Prediction Using Scikit Learn Keras And Tensorflow With Python Gui
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HEPATITIS C: Classification and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
In this comprehensive project focusing on Hepatitis C classification and prediction, the journey begins with a meticulous exploration of the dataset. Through Python, Scikit-Learn, Keras, and TensorFlow, the project aims to develop an effective model to predict Hepatitis C based on given features. The dataset's attributes are systematically examined, and their distributions are analyzed to uncover insights into potential correlations and patterns. The subsequent step involves categorizing the feature distributions. This phase sheds light on the underlying characteristics of each attribute, facilitating the understanding of their roles in influencing the target variable. This categorization lays the foundation for feature scaling and preprocessing, ensuring that the data is optimized for machine learning. The core of the project revolves around the development of machine learning models. Employing Scikit-Learn, various classification algorithms are applied, including K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Naive Bayes, Gradient Boosting, AdaBoost, Light Gradient Boosting, Multi-Layer Perceptron, and XGBoost. The models are fine-tuned using Grid Search to optimize hyperparameters, enhancing their performance and generalization capability. Taking the project a step further, deep learning techniques are harnessed to tackle the Hepatitis C classification challenge. A key component is the construction of an Artificial Neural Network (ANN) using Keras and TensorFlow. This ANN leverages layers of interconnected nodes to learn complex patterns within the data. LSTM, FNN, RNN, DBN, and Autoencoders are also explored, offering a comprehensive understanding of deep learning's versatility. To evaluate the models' performances, an array of metrics are meticulously employed. Metrics such as accuracy, precision, recall, F1-score, and AUC-ROC are meticulously calculated. The significance of each metric is meticulously explained, underpinning the assessment of a model's true predictive power and its potential weaknesses. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. The culmination of the project involves the creation of a user-friendly Graphical User Interface (GUI) using PyQt. The GUI enables users to interact seamlessly with the models, facilitating data input, model selection, and prediction execution. A detailed description of the GUI's components, including buttons, checkboxes, and interactive plots, highlights its role in simplifying the entire classification process. In a comprehensive journey of exploration, experimentation, and analysis, this project effectively marries data science and machine learning. By thoroughly examining the dataset, engineering features, utilizing a diverse range of machine learning models, harnessing the capabilities of deep learning, evaluating performance metrics, and creating an intuitive GUI, the project encapsulates the multi-faceted nature of modern data-driven endeavors.
Data Science and Deep Learning Workshop For Scientists and Engineers by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
WORKSHOP 1: In this workshop, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset with PyQt. You will build a GUI application for this purpose. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset provided by Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle (https://www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpose. WORKSHOP 2: In this workshop, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. WORKSHOP 3: In this workshop, you will implement deep learning on detecting vehicle license plates, recognizing sign language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate Detection dataset provided by Kaggle (https://www.kaggle.com/andrewmvd/car-plate-detection/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by Kaggle (https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle (https://www.kaggle.com/arunrk7/surface-crack-detection/download). WORKSHOP 4: In this workshop, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). WORKSHOP 5: In this workshop, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). WORKSHOP 6: In this worksshop, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle (https://www.kaggle.com/fedesoriano/traffic-prediction-dataset/download). This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle (https://www.kaggle.com/rashikrahmanpritom/heart-attack-analysis-prediction-dataset/download). WORKSHOP 7: In this workshop, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle (https://www.kaggle.com/ishandutta/early-stage-diabetes-risk-prediction-dataset/download). This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle (https://www.kaggle.com/merishnasuwal/breast-cancer-prediction-dataset/download). Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. WORKSHOP 8: In this workshop, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy. WORKSHOP 9: In this workshop, you will learn how to use Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, and other libraries to perform COVID-19 Epitope Prediction using COVID-19/SARS B-cell Epitope Prediction dataset provided in Kaggle. All of three datasets consists of information of protein and peptide: parent_protein_id : parent protein ID; protein_seq : parent protein sequence; start_position : start position of peptide; end_position : end position of peptide; peptide_seq : peptide sequence; chou_fasman : peptide feature; emini : peptide feature, relative surface accessibility; kolaskar_tongaonkar : peptide feature, antigenicity; parker : peptide feature, hydrophobicity; isoelectric_point : protein feature; aromacity: protein feature; hydrophobicity : protein feature; stability : protein feature; and target : antibody valence (target value). The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, Gradient Boosting, XGB classifier, and MLP classifier. Then, you will learn how to use sequential CNN and VGG16 models to detect and predict Covid-19 X-RAY using COVID-19 Xray Dataset (Train & Test Sets) provided in Kaggle. The folder itself consists of two subfolders: test and train. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy. WORKSHOP 10: In this workshop, you will learn how to use Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, and other libraries to perform analyzing and predicting stroke using dataset provided in Kaggle. The dataset consists of attribute information: id: unique identifier; gender: "Male", "Female" or "Other"; age: age of the patient; hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension; heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease; ever_married: "No" or "Yes"; work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed"; Residence_type: "Rural" or "Urban"; avg_glucose_level: average glucose level in blood; bmi: body mass index; smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"; and stroke: 1 if the patient had a stroke or 0 if not. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy. WORKSHOP 11: In this workshop, you will learn how to use Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, and other libraries to perform classifying and predicting Hepatitis C using dataset provided by UCI Machine Learning Repository. All attributes in dataset except Category and Sex are numerical. Attributes 1 to 4 refer to the data of the patient: X (Patient ID/No.), Category (diagnosis) (values: '0=Blood Donor', '0s=suspect Blood Donor', '1=Hepatitis', '2=Fibrosis', '3=Cirrhosis'), Age (in years), Sex (f,m), ALB, ALP, ALT, AST, BIL, CHE, CHOL, CREA, GGT, and PROT. The target attribute for classification is Category (2): blood donors vs. Hepatitis C patients (including its progress ('just' Hepatitis C, Fibrosis, Cirrhosis). The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and ANN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performace of the model, scalability of the model, training loss, and training accuracy.
The Practical Guides on Deep Learning Using SCIKIT-LEARN, KERAS, and TENSORFLOW with Python GUI by Vivian Siahaan,Rismon Hasiholan Sianipar Pdf
In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display image histogram. It is a graphical representation that displays the distribution of pixel intensities in an image. It provides information about the frequency of occurrence of each intensity level in the image. The histogram allows us to understand the overall brightness or contrast of the image and can reveal important characteristics such as dynamic range, exposure, and the presence of certain image features. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. The MNIST dataset is a widely used dataset in machine learning and computer vision, particularly for image classification tasks. It consists of a collection of handwritten digits from zero to nine, where each digit is represented as a 28x28 grayscale image. The dataset was created by collecting handwriting samples from various individuals and then preprocessing them to standardize the format. Each image in the dataset represents a single digit and is labeled with the corresponding digit it represents. The labels range from 0 to 9, indicating the true value of the handwritten digit. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset. Following are the steps taken in this chapter: Dataset Exploration: Explore the Brain Image MRI dataset from Kaggle. Describe the structure of the dataset, the different classes (tumor vs. non-tumor), and any preprocessing steps required; Data Preprocessing: Preprocess the dataset to prepare it for model training. This may include tasks such as resizing images, normalizing pixel values, splitting data into training and testing sets, and creating labels; Model Building: Use TensorFlow and Keras to build a deep learning model for brain tumor detection. Choose an appropriate architecture, such as a convolutional neural network (CNN), and configure the model layers; Model Training: Train the brain tumor detection model using the preprocessed dataset. Specify the loss function, optimizer, and evaluation metrics. Monitor the training process and visualize the training/validation accuracy and loss over epochs; Model Evaluation: Evaluate the trained model on the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Prediction and Visualization: Use the trained model to make predictions on new MRI images. Visualize the predicted results alongside the ground truth labels to demonstrate the effectiveness of the model. Finally, you will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle using MobileNetV2 and CNN models. Following are the steps taken in this chapter: Data Exploration: Load the dataset using Pandas, perform exploratory data analysis (EDA) to gain insights into the data, and visualize the distribution of gender classes; Data Preprocessing: Preprocess the dataset by performing necessary transformations, such as resizing images, converting labels to numerical format, and splitting the data into training, validation, and test sets; Model Building: Use TensorFlow and Keras to build a gender classification model. Define the architecture of the model, compile it with appropriate loss and optimization functions, and summarize the model's structure; Model Training: Train the model on the training set, monitor its performance on the validation set, and tune hyperparameters if necessary. Visualize the training history to analyze the model's learning progress; Model Evaluation: Evaluate the trained model's performance on the test set using various metrics such as accuracy, precision, recall, and F1 score. Generate a classification report and a confusion matrix to assess the model's performance in detail; Prediction and Visualization: Use the trained model to make gender predictions on new, unseen data. Visualize a few sample predictions along with the corresponding images. Finally, you will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset using CNN model. The FER2013 dataset contains facial images categorized into seven different emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. To perform facial expression recognition using this dataset, you would typically follow these steps; Data Preprocessing: Load and preprocess the dataset. This may involve resizing the images, converting them to grayscale, and normalizing the pixel values; Data Split: Split the dataset into training, validation, and testing sets. The training set is used to train the model, the validation set is used to tune hyperparameters and evaluate the model's performance during training, and the testing set is used to assess the final model's accuracy; Model Building: Build a deep learning model using TensorFlow and Keras. This typically involves defining the architecture of the model, selecting appropriate layers (such as convolutional layers, pooling layers, and fully connected layers), and specifying the activation functions and loss functions; Model Training: Train the model using the training set. This involves feeding the training images through the model, calculating the loss, and updating the model's parameters using optimization techniques like backpropagation and gradient descent; Model Evaluation: Evaluate the trained model's performance using the validation set. This can include calculating metrics such as accuracy, precision, recall, and F1 score to assess how well the model is performing; Model Testing: Assess the model's accuracy and performance on the testing set, which contains unseen data. This step helps determine how well the model generalizes to new, unseen facial expressions; Prediction: Use the trained model to make predictions on new images or live video streams. This involves detecting faces in the images using OpenCV, extracting facial features, and feeding the processed images into the model for prediction. Then, you will also build a GUI application for this purpose.
Machine Learning with Health Care Perspective by Vishal Jain,Jyotir Moy Chatterjee Pdf
This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.
Pattern Recognition by Karina Mariela Figueroa Mora,Juan Anzurez Marín,Jaime Cerda,Jesús Ariel Carrasco-Ochoa,José Francisco Martínez-Trinidad,José Arturo Olvera-López Pdf
This book constitutes the proceedings of the 12th Mexican Conference on Pattern Recognition, MCPR 2020, which was due to be held in Morelia, Mexico, in June 2020. The conference was held virtually due to the COVID-19 pandemic. The 31 papers presented in this volume were carefully reviewed and selected from 67 submissions. They were organized in the following topical sections: pattern recognition techniques; image processing and analysis; computer vision; industrial and medical applications of pattern recognition; natural language processing and recognition; artificial intelligence techniques and recognition.
Information Management and Big Data by Juan Antonio Lossio-Ventura,Denisse Muñante,Hugo Alatrista-Salas Pdf
This book constitutes the refereed proceedings of the 5th International Conference on Information Management and Big Data, SIMBig 2018, held in Lima, Peru, in September 2018. The 34 papers presented were carefully reviewed and selected from 101 submissions. The papers address issues such as data mining, artificial intelligence, Natural Language Processing, information retrieval, machine learning, web mining.
Understanding COVID-19: The Role of Computational Intelligence by Janmenjoy Nayak,Bighnaraj Naik,Ajith Abraham Pdf
This book provides a comprehensive description of the novel coronavirus infection, spread analysis, and related challenges for the effective combat and treatment. With a detailed discussion on the nature of transmission of COVID-19, few other important aspects such as disease symptoms, clinical application of radiomics, image analysis, antibody treatments, risk analysis, drug discovery, emotion and sentiment analysis, virus infection, and fatality prediction are highlighted. The main focus is laid on different issues and futuristic challenges of computational intelligence techniques in solving and identifying the solutions for COVID-19. The book drops radiance on the reasons for the growing profusion and complexity of data in this sector. Further, the book helps to focus on further research challenges and directions of COVID-19 for the practitioners as well as researchers.
Trends and Innovations in Information Systems and Technologies by Álvaro Rocha,Hojjat Adeli,Luís Paulo Reis,Sandra Costanzo,Irena Orovic,Fernando Moreira Pdf
This book gathers selected papers presented at the 2020 World Conference on Information Systems and Technologies (WorldCIST’20), held in Budva, Montenegro, from April 7 to 10, 2020. WorldCIST provides a global forum for researchers and practitioners to present and discuss recent results and innovations, current trends, professional experiences with and challenges regarding various aspects of modern information systems and technologies. The main topics covered are A) Information and Knowledge Management; B) Organizational Models and Information Systems; C) Software and Systems Modeling; D) Software Systems, Architectures, Applications and Tools; E) Multimedia Systems and Applications; F) Computer Networks, Mobility and Pervasive Systems; G) Intelligent and Decision Support Systems; H) Big Data Analytics and Applications; I) Human–Computer Interaction; J) Ethics, Computers & Security; K) Health Informatics; L) Information Technologies in Education; M) Information Technologies in Radiocommunications; and N) Technologies for Biomedical Applications.
Intelligent Computing Methodologies by De-Shuang Huang,Prashan Premaratne Pdf
This two-volume set of LNCS 12463 and LNCS 12464 constitutes - in conjunction with the volume LNAI 12465 - the refereed proceedings of the 16th International Conference on Intelligent Computing, ICIC 2020, held in Bari, Italy, in October 2020. The 162 full papers of the three proceedings volumes were carefully reviewed and selected from 457 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, addressing theories, methodologies, and applications in science and technology.
Argumentation Mining by Manfred Stede,Jodi Schneider Pdf
Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a--necessarily subjective--outlook for the field.
Digital Personalized Health and Medicine by L.B. Pape-Haugaard,C. Lovis,I. Cort Madsen Pdf
Digital health and medical informatics have grown in importance in recent years, and have now become central to the provision of effective healthcare around the world. This book presents the proceedings of the 30th Medical Informatics Europe conference (MIE). This edition of the conference, hosted by the European Federation for Medical Informatics (EFMI) since the 1970s, was due to be held in Geneva, Switzerland in April 2020, but as a result of measures to prevent the spread of the Covid19 pandemic, the conference itself had to be cancelled. Nevertheless, because this collection of papers offers a wealth of knowledge and experience across the full spectrum of digital health and medicine, it was decided to publish the submissions accepted in the review process and confirmed by the Scientific Program Committee for publication, and these are published here as planned. The 232 papers are themed under 6 section headings: biomedical data, tools and methods; supporting care delivery; health and prevention; precision medicine and public health; human factors and citizen centered digital health; and ethics, legal and societal aspects. A 7th section deals with the Swiss personalized health network, and section 8 includes the 125 posters accepted for the conference. Offering an overview of current trends and developments in digital health and medical informatics, the book provides a valuable information resource for researchers and health practitioners alike.
The pharmaceutical industry is almost boundless in its ability to supply new drug therapies, but how does one decide which are the best medicines to use within restricted budgets? With particular emphasis on modeling, methodologies, data sources, and application to real-world dilemmas, Pharmacoeconomics: From Theory to Practice provides an introduc
An Intuitive Exploration of Artificial Intelligence by Simant Dube Pdf
This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential. The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.
Artificial Intelligence in Medicine by David Riaño,Szymon Wilk,Annette ten Teije Pdf
This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.
Applied Information Processing Systems by Brijesh Iyer,Debashis Ghosh,Valentina Emilia Balas Pdf
This book is a collection of selected high-quality research papers presented at the International Conference on Computing in Engineering and Technology (ICCET 2021), organized by Dr. Babasaheb Ambedkar Technological University, Lonere, India, during January 30–31, 2021. Focusing on frontier topics and next-generation technologies, it presents original and innovative research from academics, scientists, students and engineers alike. The theme of the conference is Applied Information Processing System.
