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Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

This paper is devoted to the relationship between news sentiment and changes in housing market movements. It provides a novel and straightforward approach to account for heterogeneous expectations of market actors within a probabilistic framework utilizing machine learning. Our novel sentiment index shows a persistent and statistically significant explanatory power for the prediction of the housing market, in contrast to common dictionary approaches. This holds for news headlines and abstracts and different definitions of sentiment indices. Our results can be regarded as the first sentiment-based evidence of heterogeneous actors in the housing market and underline the importance of different expectations for measuring non-fundamental drivers. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap image-level annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them. We propose an Inductive Attention Guidance Network (IAG-Net) to jointly learn a global image-level classifier for normal/PDAC classification and a local voxel-level classifier for semi-supervised PDAC segmentation. We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier. Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by more than 5% compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Electricity usage of buildings (including offices, malls, and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. In the United States, the buildings' appliances consume 72% of the total produced electricity approximately. In this regard, cyber‐physical system (CPS) researchers have put forth associated research questions to reduce cyber‐physical building environment energy consumption by minimizing the energy dissipation while securing occupants' comfort. Some of the questions in CPS building include finding the optimal HVAC control, monitoring appliances' energy usage, detecting insulation problems, estimating the occupants' number and activities, managing thermal comfort, intelligently interacting with the smart grid. Various machine learning (ML) applications have been studied in recent CPS researches to improve building energy efficiency by addressing these questions. In this paper, we comprehensively review and report on the contemporary applications of ML algorithms such as deep learning, transfer learning, active learning, reinforcement learning, and other emerging techniques that propose and envision to address the above challenges in the CPS building environment. Finally, we conclude this article by discussing diverse existing open questions and prospective future directions in the CPS building environment research. This article is categorized under:Technologies > Machine LearningTechnologies > Reinforcement Learning [ABSTRACT FROM AUTHOR]

Tornado outbreaks in the United States have caused large numbers of human and economic losses. The analysis of gridded climate data to find spatial patterns of meteorological variables that produce tornadoes is often a time consuming and manual process requiring visual inspection. Therefore, the ability to train convolutional neural networks (CNNs) to recognize patterns in climate data that produce tornado outbreaks would be a huge benefit to climatologists. In this paper, three CNNs of increasing complexity, including LeNet-5, VGG-16, and Resnet-50 were trained to predict days with tornado outbreaks of varying size based on gridded values of convective available potential energy (CAPE), convective inhibition (CIN), storm relative helicity (HLCY), and geopotential height. All three models showed promising results in predicting large tornado outbreaks with greater than 20 tornadoes with VGG-16 using the variable CAPE having a slightly better performance, highlighting the importance of CAPE in the analysis of tornado environments. • CNN models and climate grids were used to predict tornado outbreaks in the US. • A feature engineering pipeline was developed to enhance image features. • Models were trained on individual climate variables and all variables combined. • The combination of all variables with VGG-16 produced the best overall result. • VGG-16 with CAPE was best at predicting days with 20 or greater tornadoes. [ABSTRACT FROM AUTHOR]