The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

Emotion and Adaptation

Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

Human activity recognition systems are developed as part of a framework to enable continuous monitoring of human behaviours in the area of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and surveillance in smart home environments. The extraction of relevant features is the most challenging part of the mobile and wearable sensor-based human activity recognition pipeline. Feature extraction influences the algorithm performance and reduces computation time and complexity. However, current human activity recognition relies on handcrafted features that are incapable of handling complex activities especially with the current influx of multimodal and high dimensional sensor data. With the emergence of deep learning and increased computation powers, deep learning and artificial intelligence methods are being adopted for automatic feature learning in diverse areas like health, image classification, and recently, for feature extraction and classification of simple and complex human activity recognition in mobile and wearable sensors. Furthermore, the fusion of mobile or wearable sensors and deep learning methods for feature learning provide diversity, offers higher generalisation, and tackles challenging issues in human activity recognition. The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition. The review presents the methods, uniqueness, advantages and their limitations. We not only categorise the studies into generative, discriminative and hybrid methods but also highlight their important advantages. Furthermore, the review presents classification and evaluation procedures and discusses publicly available datasets for mobile sensor human activity recognition. Finally, we outline and explain some challenges to open research problems that require further research and improvements.

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

In this paper, an advanced novel heuristic algorithm is presented, the hybrid genetic algorithm with neural networks (HGA-NN), which is used to identify an optimum feature subset and to increase the classification accuracy and scalability in credit risk assessment. This algorithm is based on the following basic hypothesis: the high-dimensional input feature space can be preliminarily restricted to only the important features. In this preliminary restriction, fast algorithms for feature ranking and earlier experience are used. Additionally, enhancements are made in the creation of the initial population, as well as by introducing an incremental stage in the genetic algorithm. The performances of the proposed HGA-NN classifier are evaluated using a real-world credit dataset that is collected at a Croatian bank, and the findings are further validated on another real-world credit dataset that is selected in a UCI database. The classification accuracy is compared with that presented in the literature. Experimental results that were achieved using the proposed novel HGA-NN classifier are promising for feature selection and classification in retail credit risk assessment and indicate that the HGA-NN classifier is a promising addition to existing data mining techniques.

https://bib.irb.hr/datoteka/640660.Genetic_algorithm-based_heuristic_for_feature_selection_in_credit_risk.pdf

Genetic algorithm-based heuristic for feature selection in credit risk assessment

Document Segmentation is a process that aims to filter documents while identifying certain regions of interest. Generally, the regions of interest include texts, graphics (image occupied regions) and the background. This paper presents a novel top-bottom approach to perform document segmentation using texture features that are extracted from the specified/selected documents. A mask of suitable size is used to summarize textural features, and statistical parameters are captured as blocks in document images. Four textural features that are extracted from masks using the gray level co-occurrence matrix (glcm) include entropy, contrast, energy and homogeneity. Furthermore, two statistical parameters extracted from corresponding masks are the modal and median pixel values. The extracted attributes allow the classification of each mask or block as text, graphics, and background. A feedforward network is trained on the 6 extracted attributes, using documents obtained from a public database ; an error rate of 15.77 % is achieved. Furthermore, it is shown that this novel approach produces promising performance in segmenting documents and is expected to be significantly efficient for content-based information retrieval systems. Detection of duplicate documents within large databases is another potential area of application.

Document segmentation using textural features summarization and feedforward neural network

The price of crude oil is tied to major economic activities in all nations of the world, as a change in the price of crude oil invariably affects the cost of other goods and services. This has made the prediction of crude oil price a top priority for researchers and scientists alike. In this paper we present an intelligent system that predicts the price of crude oil. This system is based on Support Vector Machines. Support Vector Machines are supervised learners founded upon the principle of statistical learning theory. Our system utilized as its input key economic indicators which affect the price of crude oil and has as its output the price of crude oil. Data for our system was obtained from the West Texas Intermediate (WTI) dataset spanning 24 years and experimental results obtained were very promising as it proved that support vector machines could be used with a high degree of accuracy in predicting crude oil price.

Intelligent prediction of crude oil price using Support Vector Machines

Nowadays, an identification system is needed in the food processing industries to boost the efficiency of production so as to meet up with demand in the society. Manual approach is often used in product grading and quality control, and this unfortunately could lead to uneven products, higher time expense, and fatigue by the human operators. Therefore, we propose in this article, an automatic system for classification of banana whether it is healthy for production or not. Such a system is faster, accurate and also relieves the stress that an operator may have. Our system uses GLCM texture feature analysis to extract the features required for training and testing three classification models; namely, radial basis function (RBF), support vector machine (SVM), and backpropagation neural network (ANN). A classification performance comparison is drawn between the different classification models, and the obtained experimental results indicate that such intelligent grading systems may be efficiently used in real life applications for similar tasks in food processing industries.

Practical applications
The Automatic system is highly needed in food processing industries to meet up with the production of food products required in the societies. Healthy food products are needed in the society and this can be achieved by implementing a system that will enhance in the sorting or grading of the raw materials (such as banana) used in food processing industries. This system is accurate, economical, and faster in achieving the best product. Such a system will make the product be readily available in the market i.e. meeting the need of the people.

Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations

The idea of machines having emotions sounds like science fiction, however, few decades ago the idea of machines with intelligence seemed also like fiction, but today we are developing intelligent machines with successful applications. We have always overlooked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model certain emotions in machine learning. This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The performances of the EmNN and a conventional BP-based neural network, using two topologies for each network, will be compared when applied to a blood cell type identification problem. Experimental results show that the additional emotional parameters and weights improved the identification rate as well as the classification time.

Blood Cell Identification Using Emotional Neural Networks

The early identification of leukemia form in cancer patients can greatly increase the likelihood of recovery. Diagnostic methods that distinguish among the disease's many forms are either costly or do not exist. Amongst the existing diagnostic methods are immune-phenotype and cytogenetic abnormality, which require time to obtain results and are costly to perform due to their requirement of well equipped laboratories. Thus, there is a need for fast and cost-effective method that results in the identification of the different leukemia forms or types. Therefore, we propose the use of morphological analysis of microscopic images of leukemic blood cells for the identification purpose. We present in this paper the first phase of an automated leukemia form identification system, which is the segmentation of infected cell images. The segmentation process provides two enhanced images for each blood cell; containing the cytoplasm and the nuclei regions. Unique features for each form of leukemia can then be extracted from the two images and used for identification.

http://www.wseas.us/e-library/conferences/2010/Harvard/CEA/CEA-15.pdf

Adnan Khashman

Papers

Document segmentation using textural features summarization and feedforward neural network

Intelligent prediction of crude oil price using Support Vector Machines

Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations

Blood Cell Identification Using Emotional Neural Networks

Image segmentation of blood cells in leukemia patients