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

Training a neural network involves the adaptation of its internal parameters for modelling a specific task. The states of the internal parameters during training describe how much experiential knowledge the model has acquired. Although, it is desirable that a trained neural network achieves zero classification error on the training examples while tuning its internal parameters for a task, the amount of generalisation power that is lost while enforcing such a learning constraint on the model is quite important. In this paper, we review from a practical perspective the consequences of enforcing such a learning constraint which results in a model that has learned a smooth mapping function or essentially 'memorised' the training data. In addition, we investigate how the curse of dimensionality relates to such a learning constraint. For our experiments, we consider handwritten character recognition applications using publicly available datasets.

A simple and practical review of over-fitting in neural network learning

Over the past four years there has been a marginal increase in research on developing advanced information technologies that can be efficiently used for national and international security in our war against terrorism. The list of wanted persons who are still free is getting larger, however, in most cases there is a database containing their face images and this can be used in the development of face recognition systems. A human face is an extremely complex object with features that can vary over time, sometimes very rapidly. This paper presents a fast intelligent face recognition system that uses essential face features averaging and a neural network to identify multi-expression faces. A real life application using this method is implemented on 180 images of 30 persons. Experimental results suggest that this simple but efficient system performs well, thus providing a fast intelligent system for recognizing faces with different expressions.

Intelligent face recognition using feature averaging

Medical imaging requires storage of large quantities of digitized data Efficient storage and transmission of medical images in telemedicine is of utmost importance however,. Due to the constrained bandwidth and storage capacity, a medical image must be compressed before transmission or storage. An ideal image compression system must yield high quality compressed images with high compression ratio; this can be achieved using DCT-based image compression, however the contents of the image affects the choice of an optimum compression ratio. In this paper, a neural network is trained to relate the x-ray image contents to their optimum compression ratio. Once trained, the optimum DCT compression ratio of the x-ray image can be chosen upon presenting the image to the network. Experimental results suggest that out proposed system, can be efficiently used to compress x-rays while maintaining high image quality.

Optimum dct compression of medical images using neural networks

Modeling people’s anticipation for Cyprus peace mediation outcome using a neural model

The key problem in any decision-making system is to gather as much information as possible about the object or the phenomenon under study. In the case of the radar targets the frequency and angular information is integrated to form a radar image, which has high information content. A supperresolution technique (MUSIC 2D) is used in the paper in order to reconstruct the target image. A supervised self organizing neural network was developed to classify the images obtained in this way for ten different radar targets in an anechoic chamber.

Adnan Khashman

Papers

A simple and practical review of over-fitting in neural network learning

Intelligent face recognition using feature averaging

Optimum dct compression of medical images using neural networks

Modeling people’s anticipation for Cyprus peace mediation outcome using a neural model

Multiple signal classification (MUSIC 2D) technique and self-organizing neural network applied in target radiolocation recognition