HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features.

Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This scheme is based on a strategy of aggregating handcrafted and deep CNN features.

Efficient multimodal deep-learning-based COVID-19 diagnostic system for noisy and corrupted images.

Introduction: In humanity's ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination.

Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage.

5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics.

Biosynthesis of silver nanoparticles using Haloferax sp. NRS1: image analysis, characterization, in vitro thrombolysis and cytotoxicity.

Haloferax sp strain NRS1 (MT967913) was isolated from a solar saltern on the southern coast of the Red Sea, Jeddah, Saudi Arabia. The present study was designed for estimate the potential capacity of the Haloferax sp strain NRS1 to synthesize (silver nanoparticles) AgNPs. Biological activities such as thrombolysis and cytotoxicity of biosynthesized AgNPs were evaluated.

Remote diagnostic and detection of coronavirus disease (COVID-19) system based on intelligent healthcare and internet of things.

In this paper, we will propose a novel system for remote detecting COVID-19 patients based on artificial intelligence technology and internet of things (IoT) in order to stop the virus spreading at an early stage. In this work, we will focus on connecting several sensors to work together as a system that can discover people infected with the Coronavirus remotely, this will reduce the spread of the disease. The proposed system consists of several devices called .

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals.

Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information.

Simultaneous bioremediation of cationic copper ions and anionic methyl orange azo dye by brown marine alga Fucus vesiculosus.

Textile wastewater contains large quantities of azo dyes mixed with various contaminants especially heavy metal ions. The discharge of effluents containing methyl orange (MO) dye and Cu ions into water is harmful because they have severe toxic effects to humans and the aquatic ecosystem. The dried algal biomass was used as a sustainable, cost-effective and eco-friendly for the treatment of the textile wastewater.

Machine learning based classification of normal, slow and fast walking by extracting multimodal features from stride interval time series.

The gait speed affects the gait patterns (biomechanical and spatiotemporal parameters) of distinct age populations. Classification of normal, slow and fast walking is fundamental for understanding the effects of gait speed on the gait patterns and for proper evaluation of alternations associated with it. In this study, we extracted multimodal features such as time domain and entropy-based complexity measures from stride interval signals of healthy subjects moving with normal, slow and fast speeds.

Machine learning based congestive heart failure detection using feature importance ranking of multimodal features.

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects.

Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification.

Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications.

An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set.

Background: Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images.

Objective: Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system.

Fungicidal Impact of L. (Miswak) Extract on Growth of Foodborne Pathogens, Species.

Several investigations have focused on studying the suppressing influence of (miswak) on oral microbes; however, studies regarding its fungicidal activity versus human aspergillosis-related illness are still scarce. The current research was designed to evaluate the fungicidal action of aquatic root extract in terms of radial growth rate and inhibition zone (IZO) versus 3 pathogenic species, namely, r, , and in vitro. The results revealed that the plant extract (50 and 100 mg/mL) exhibited a prohibiting influence on the growth of the tested fungal species.

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction.

Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks.