An efficient human activity recognition model based on deep learning approaches

Human Activity Recognition (HAR) has gained traction in recent years in diverse areas such as observation, entertainment, teaching and healthcare, using wearable and smartphone sensors. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Different developed models for HAR have been explained in literature. Deep learning systems and algorithms were shown to perform highly in HAR in recent years, but these algorithms need lots of computerization to be deployed efficiently in applications. This paper presents a HAR lightweight, low computing capacity, deep learning model, which is ideal for use in real-time applications. The generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains and standard Convolutional Neural Network (CNN) used for classification. The findings demonstrate that many of the deployed deep learning and machine learning techniques are surpassed by the proposed model. TRANSLATE with x English ArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back /

Segmentation and measurement of lung pathological changes for COVID-19 diagnosis based on computed tomography

Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19

Isolation of proteus spp. bacterial pathogens from raw minced meat in Alkarkh area, Baghdad provelance

The aim of the study was to isolate proteus bacteria species from raw minced meat obtained from some butcheries in Alkarkh area. A total of 60 raw minced meat samples were collected from meat shops in the Alkarkh area-Baghdad Province/Iraq. The samples were analyzed for the presence of proteus spp. bacteria using MacConkey, Blood, and Selective agar. A total of isolates that satisfied the preliminary biochemical tests were further confirmed by using the API 20E assay, Vitek2 Diagnostic Method, and a final confirmation test by polymerase chain reaction (PCR). The proportion of proteus spp. bacteria was 8.3% obtained from all the bacteria isolated in minced meat samples. Although most of the species identified are pathogenic to humans, some strains are known to foodborne outbreaks cause even in countries with proper public health facilities. It is recommended that effective food safety education and training of personnel that handle food at retail points will help to reduce the effect of these pathogens on humans

Evaluation of Anti-Bacterial Activity of Silybum Marianum Against Proteus Spp. in Minced Meat

This study was conducted to investigate the antibacterial activity of Silybum marianum (silymarin) against Proteus spp. bacteria isolated from minced meat. Two concentrations (1000 μg/ml and 2000 μg/ml) of Silybum marianum were assayed against proteus spp., by inoculating minced meat for (60 and 120 min): and stored at refrigerator temperature for 24, 72, and 120 hours, respectively; the bacterial counting was done on selective media. The results showed that proteus spp. were resistant to low concentrations of silymarin, while the inhibitory concentration of silymarin against Proteus strains was ≥ 2000 μg/ml. It was concluded that silymarin is a more beneficial antibacterial in the 2000 μg/ml concentration for the elimination of Proteus spp. pathogenic bacteria in minced meat than in the low concentratio

Advanced Estimation of Brain Age Using Pre-trained 2D Convolutional Neural Networks on a Public Dataset

This work introduces a brand-new approach to be employed for correctly assessing healthy person’s brain aging, masking what constitutes the most serious challenge in the fight against age-related cognitive decline. An approach is serviced by 2D CNNs, a simpler technology to state-of-the-art 3D model, to yield close to accurate forecast. Our algorithm improves telling in two respects. By virtue of this, we will utilize well-known ImageNet-pre-trained classifiers to suggest initial brain age predictions. This process sets the tone of the core of our business model in terms of dependability and reliability. Next, we improve the networks’ performance through progressively expanding their capacity via fine-tuning on pre-segmentation tasks using the neuroimaging datasets which are openly available. This stage increases the predictive accuracy in addition to ensuring that there is transparency and flexibility because it utilizes open datasets. Our research's strength is that it encompasses all techniques and fields necessary for brain age estimation and adopts justifiable evaluation metrics. As a result, this conduct adds to the literature. Our study not only points out deficiencies in private datasets but reels out the validity of our approach by using the public data instead to give the results another direction of accessibility and reproducibility