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

Recently, many efficient image compression methods have been developed in order to compress image data prior to further processing. Discrete cosine transforms and wavelet transforms are examples of these compression methods. The aim of the work presented within this paper is to introduce comparison criteria for optimum compression using these well known image compression techniques. The comparison criteria will comprise visual inspection and computed analysis

Comparison Criteria for Optimum Image Compression

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many “engineering” prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as “prototype-incorporated EmNN”. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

https://orbilu.uni.lu/bitstream/10993/32251/1/FINAL_VERSION.pdf

Prototype-Incorporated Emotional Neural Network

Efficient storage and transmission of medical images in telemedicine is of utmost importance however, this efficiency can be hindered due to storage capacity and constraints on bandwidth. Thus, a medical image may require compression before transmission or storage. Ideal image compression systems must yield high quality compressed images with high compression ratio; this can be achieved using wavelet transform based compression, however, the choice of an optimum compression ratio is difficult as it varies depending on the content of the image. In this paper, a neural network is trained to relate radiograph image contents to their optimum image compression ratio. Once trained, the neural network chooses the ideal Haar wavelet compression ratio of the x-ray images upon their presentation to the network. Experimental results suggest that our proposed system, can be efficiently used to compress radiographs while maintaining high image quality.

Medical radiographs compression using neural networks and Haar wavelet

Emotional processes and responses are biological and psychological processes in humans. The modelling of these biological processes in machines is considered a challenging and often controversial t...

An emotional system with application to blood cell type identification

The human ability to recognize objects has not so far been matched by intelligent machines. This is more evident when it comes to recognizing faces, where a quick human “glance” is sufficient to recognize a “familiar” face. Face recognition has recently attracted more research aimed at developing reliable recognition by machines. Current face recognition methods rely on detecting certain features within a face and using these features for face recognition. This paper introduces a novel approach to face recognition by simulating our ability to recognize “familiar” faces after a quick “glance” using pattern averaging and neural networks. A real-life application will be presented throughout recognizing the faces of 30 persons. Time costs and the neural network parameters will be described, in addition to future work aimed at further improving the developed system.

Adnan Khashman

Papers

Comparison Criteria for Optimum Image Compression

Prototype-Incorporated Emotional Neural Network

Medical radiographs compression using neural networks and Haar wavelet

An emotional system with application to blood cell type identification

Face recognition using neural networks and pattern averaging