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Building ecologically-relevant source of nourishment thresholds: Any tool-kit using tips on the employ.
Recognition of activities, such as preparing meal or watching TV, performed by a smart home resident, can promote the independent living of elderly in a safe and comfortable environment of their own homes, for an extended period of time. Different activities performed at the same location have commonalities resulting in less inter-class variations; while the same activity performed multiple times, or by multiple residents, varies in its execution resulting in high intra-class variations. We propose a Local Feature Weighting approach (LFW) that assigns weights based on both inter-class and intra-class importance of a feature in an activity. Multiple sensors are deployed at different locations in a smart home to gather information. We exploit the obtained information, such as frequency and duration of activation of sensors, and the total sensors in an activity for feature weighting. The weights for the same features vary among activities, since a feature may have more importance for one activity but less for the other. For the classification, we exploit the two variants of K-Nearest Neighbors (KNN) Evidence Theoretic KNN (ETKNN) and Fuzzy KNN (FKNN). The evaluation of the proposed approach on three datasets, from CASAS smart home project, demonstrates its ability in the correct recognition of activities compared to the existing approaches.In the current trends, face recognition has a remarkable attraction towards favorable and inquiry of an image. Several algorithms are utilized for recognizing the facial expressions, but they lack in the issues like inaccurate recognition of facial expression. To overcome these issues, a Graph-based Feature Extraction and Hybrid Classification Approach (GFE-HCA) is proposed for recognizing the facial expressions. The main motive of this work is to recognize human emotions in an effective manner. Initially, the face image is identified using the Viola-Jones algorithm. Subsequently, the facial parts such as right eye, left eye, nose and mouth are extracted from the detected facial image. The edge-based invariant transform feature is utilized to extract the features from the extracted facial parts. From this edge-based invariant features, the dimensions are optimized using Weighted Visibility Graph which produces the graph-based features. Also, the shape appearance-based features from the facial parts are extracted. From these extracted features, facial expressions are recognized and classified using a Self-Organizing Map based Neural Network Classifier. The performance of this GFE-HCA approach is evaluated and compared with the existing techniques, and the superiority of the proposed approach is proved with its increased recognition rate.Social networks have become a major platform for people to disseminate information, which can include negative rumors. In recent years, rumors on social networks has caused grave problems and considerable damages. We attempted to create a method to verify information from numerous social media messages. We propose a general architecture that integrates machine learning and open data with a Chatbot and is based cloud computing (MLODCCC), which can assist users in evaluating information authenticity on social platforms. The proposed MLODCCC architecture consists of six integrated modules cloud computing, machine learning, data preparation, open data, chatbot, and intelligent social application modules. Food safety has garnered worldwide attention. Consequently, we used the proposed MLODCCC architecture to develop a Food Safety Information Platform (FSIP) that provides a friendly hyperlink and chatbot interface on Facebook to identify credible food safety information. The performance and accuracy of three binary classification algorithms, namely the decision tree, logistic regression, and support vector machine algorithms, operating in different cloud computing environments were compared. The binary classification accuracy was 0.769, which indicates that the proposed approach accurately classifies using the developed FSIP.The SARS-CoV‑2 has infected millions of humans worldwide in the past few months and hundreds of thousands have died as a result of an infection. The end of the pandemic is not in sight and many people are anxious of becoming infected in different settings. The Gastein Healing Gallery (GHG) is a unique outpatient facility combining heat, high humidity and mild radon radiation. Every year approximately 12,000 patients with inflammatory rheumatic, degenerative diseases and chronic pain are treated. We have therefore reviewed and analyzed the literature with respect to a possible increased risk of infection for patients during treatment in the GHG. On the one hand the climatic and physical conditions in the GHG can be viewed as hostile to viruses and on the other hand the mild radon hyperthermia and the geographic location of the GHG lead to positive effects on the patient's health via complex physiological processes. We therefore consider the likelihood of infection with viruses in the GHG in no way increased, in contrast, it is probably considerably lower compared to other settings.The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. check details To support the current combat against the disease, this research aims to propose a machine learning-based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19-affected CTI using social group optimization-based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis-based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree.
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