Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification.

convolutional neural network --- image segmentation --- multi-scale feature fusion --- semantic features --- Gaofen 6 --- aerial images --- land-use --- Tai’an --- convolutional neural networks (CNNs) --- feature fusion --- ship detection --- optical remote sensing images --- end-to-end detection --- transfer learning --- remote sensing --- single shot multi-box detector (SSD) --- You Look Only Once-v3 (YOLO-v3) --- Faster RCNN --- statistical features --- Gaofen-2 imagery --- winter wheat --- post-processing --- spatial distribution --- Feicheng --- China --- light detection and ranging --- LiDAR --- deep learning --- convolutional neural networks --- CNNs --- mask regional-convolutional neural networks --- mask R-CNN --- digital terrain analysis --- resource extraction --- hyperspectral image classification --- few-shot learning --- quadruplet loss --- dense network --- dilated convolutional network --- artificial neural networks --- classification --- superstructure optimization --- mixed-inter nonlinear programming --- hyperspectral images --- super-resolution --- SRGAN --- model generalization --- image downscaling --- mixed forest --- multi-label segmentation --- semantic segmentation --- unmanned aerial vehicles --- classification ensemble --- machine learning --- Sentinel-2 --- geographic information system (GIS) --- earth observation --- on-board --- microsat --- mission --- nanosat --- AI on the edge --- CNN

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue.

pancreas --- segmentation --- computed tomography --- deep learning --- data augmentation --- neoplasm metastasis --- ovarian neoplasms --- radiation exposure --- tomography --- x-ray computed --- prostate carcinoma --- microscopic --- convolutional neural network --- machine learning --- handcrafted --- oral carcinoma --- medical image segmentation --- colon cancer --- colon polyps --- OCT --- optical biopsy --- animal rat models --- CADx --- airway volume analysis --- artificial intelligence --- coronary artery disease --- SPECT MPI scans --- convolutional neural networks --- transfer learning --- classification models --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The building industry is influenced by many factors and trends reflecting the current situation and developments in social, economic, technical, and scientific fields. One of the most important trends seeks to minimize the energy demand. This can be achieved by promoting the construction of buildings with better thermal insulating capabilities of their envelopes and better efficiency in heating, ventilation, and air conditioning systems. Any credible assessment of building energy performance includes the identification and simulation of heat and mass transfer phenomena in both the building envelope and the interior of the building. As the interaction between design elements, climate change, user behavior, heating effectiveness, ventilation, air conditioning systems, and lighting is not straightforward, the assessment procedure can present a complex and challenging task. The simulations should then involve all factors affecting the energy performance of the building in questions. However, the appropriate choice of physical model of heat and mass transfer for different building elements is not the only factor affecting the output of building energy simulations. The accuracy of the material parameters applied in the models as input data is another potential source of uncertainty. For instance, neglecting the dependence of hygric and thermal parameters on moisture content may affect the energy assessment in a significant way. Boundary conditions in the form of weather data sets represent yet another crucial factor determining the uncertainty of the outputs. In light of recent trends in climate change, this topic is vitally important. This Special Issue aims at providing recent developments in laboratory analyses, computational modeling, and in situ measurements related to the assessment of building energy performance based on the proper identification of heat and mass transfer processes in building structures.

CFD --- thermal performance --- Metamodeling --- carbon black --- energy balance --- XRD --- air terminal device --- Hygrothermal assessment --- thermal energy storage --- fibrous aerogel --- Probabilistic assessment --- natural ventilation --- thermal properties --- DSC --- advanced personalized ventilation --- temperature --- noise level --- geopolymers --- elevation --- plaster --- relative humidity --- air velocity --- ground-granulated blast-furnace slag --- heat treatment --- turbulence --- phase change temperature --- energy saving --- mechanical properties --- building envelope --- SEM --- Time series modelling --- self-heating --- mass flow rate prediction --- thermal conductivity --- Convolutional neural networks --- single-sided --- correlation function

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture.

MR brain segmentation --- fuzzy clustering --- object extraction --- silhouette analysis --- DICOM processing --- 3D modeling --- semantic segmentation --- convolutional neural networks --- kidney biopsy --- kidney transplantation --- glomerulus detection --- glomerulosclerosis --- pattern recognition --- hemoglobin --- anemia --- human tissues --- conjunctiva --- non-invasive medical device --- training size --- deep learning --- convolutional neural network --- U-Net --- segmentation --- artificial intelligence --- digital pathology --- kidney fibrosis --- blood vessel segmentation --- inferior vena cava --- ultrasound imaging --- binary tree model --- pulsatility --- fluid volume assessment --- n/a

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

multiple cancer types --- integrative analysis --- omics data --- prognosis modeling --- classification --- gene set enrichment analysis --- boosting --- kernel method --- Bayes factor --- Bayesian mixed-effect model --- CpG sites --- DNA methylation --- Ordinal responses --- GEE --- lipid–environment interaction --- longitudinal lipidomics study --- penalized variable selection --- convolutional neural networks --- deep learning --- feed-forward neural networks --- machine learning --- gene regulatory network --- nonparanormal graphical model --- network substructure --- false discovery rate control --- gaussian finite mixture model --- clustering analysis --- uncertainty --- expectation-maximization algorithm --- classification boundary --- gene expression --- RNA-seq --- n/a --- lipid-environment interaction