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

Hand gesture for communication has proven effective for humans, and active research is ongoing in replicating the same success in computer vision systems. Human–computer interaction can be significantly improved from advances in systems that are capable of recognizing different hand gestures. In contrast to many earlier works, which consider the recognition of significantly differentiable hand gestures, and therefore often selecting a few gestures from the American Sign Language (ASL) for recognition, we propose applying deep learning to the problem of hand gesture recognition for the whole 24 hand gestures obtained from the Thomas Moeslund’s gesture recognition database. We show that more biologically inspired and deep neural networks such as convolutional neural network and stacked denoising autoencoder are capable of learning the complex hand gesture classification task with lower error rates. The considered networks are trained and tested on data obtained from the above-mentioned public database; results comparison is then made against earlier works in which only small subsets of the ASL hand gestures are considered for recognition.

Deep learning in vision-based static hand gesture recognition

This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases; each case with 24 numerical attributes; based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance; where it may be used efficiently, and quickly in automatic processing of credit applications.

Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

Much of the research work into artificial intelligence (AI) has been focusing on exploring various potential applications of intelligent systems with successful results in most cases. In our attempts to model human intelligence by mimicking the brain structure and function, we overlook an important aspect in human learning and decision making: the emotional factor. While it currently sounds impossible to have ldquomachines with emotions,rdquo it is quite conceivable to artificially simulate some emotions in machine learning. This paper presents a modified backpropagation (BP) learning algorithm, namely, the emotional backpropagation (EmBP) learning algorithm. The new algorithm has additional emotional weights that are updated using two additional emotional parameters: anxiety and confidence. The proposed ldquoemotionalrdquo neural network will be implemented to a facial recognition problem, and the results will be compared to a similar application using a conventional neural network. Experimental results show that the addition of the two novel emotional parameters improves the performance of the neural network yielding higher recognition rates and faster recognition time.

A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients

Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. Artificial neural networks (NNs) have been considered to be accurate tools for credit analysis among others in the credit industry. Lately, emotional neural networks (EmNNs) have been suggested and applied successfully for pattern recognition. In this paper we investigate the efficiency of EmNNs and compare their performance to conventional NNs when applied to credit risk evaluation. In total 12 neural networks; based equally on emotional and conventional neural models; are arbitrated under three learning schemes to classify whether a credit application is approved or declined. The learning schemes differ in the ratio of training-to-validation data used during training and testing the neural networks. The emotional and conventional neural models are trained using real world credit application cases from the Australian credit approval datasets which has 690 cases; each case with 14 numerical attributes; based on which an application is accepted or rejected. The performance of the 12 neural networks will be evaluated using certain criteria. Experimental results suggest that both emotional and conventional neural models can be used effectively for credit risk evaluations, however the emotional models outperform their conventional counterparts in decision making speed and accuracy, thus, making them ideal for implementation in fast automatic processing of credit applications.

Credit risk evaluation using neural networks: Emotional versus conventional models

Wavelet-based image compression provides substantial improvements in picture quality at higher compression ratios. Haar wavelet transform based compression is one of the methods that can be applied for compressing images. An ideal image compression system must yield good quality compressed images with good compression ratio, while maintaining minimal time cost. With Wavelet transform based compression, the quality of compressed images is usually high, and the choice of an ideal compression ratio is difficult to make as it varies depending on the content of the image. Therefore, it is of great advantage to have a system that can determine an optimum compression ratio upon presenting it with an image. We propose that neural networks can be trained to establish the non-linear relationship between the image intensity and its compression ratios in search for an optimum ratio. This paper suggests that a neural network could be trained to recognize an optimum ratio for Haar wavelet compression of an image upon presenting the image to the network. Two neural networks receiving different input image sizes are developed in this work and a comparison between their performances in finding optimum Haar-based compression is presented.

/pdf/image-compression-using-neural-networks-and-haar-wavelet-biqdrlk2go.pdf

Adnan Khashman

Papers

Deep learning in vision-based static hand gesture recognition

Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients

Credit risk evaluation using neural networks: Emotional versus conventional models

Image compression using neural networks and haar wavelet