Video Recommendation System for YouTube Considering Users Feedback

Youtube is the most video sharing and viewing platform in the world. As there are many people of different tastes, hundreds of categories of videos can be found on YouTube while thousands of videos of each. So, when the site recommends videos for a user it takes some issues which fill the needs of the user. Most of the time a user watches videos related to the previously watched video. But sometimes userFFFD;s mood changes with time or weather. A user may not hear a song in the whole year but can search the song on a rainy day. Another case a user may watch some types of videos at day but another type of videos at night or another at midnight. In this paper, we propose a recommendation system considering some attributes like weather, time, month to understand the dynamic mood of a user. Each attribute is assigned a weight calculated by performing a survey on some YouTube users. Most recently viewed videos is assigned heavy weight and weather is assigned lower. This recommendation system will make YouTube more user-friendly, dynamic and acceptable

Computer Vision Based Traffic Monitoring and Analyzing From On-Road Videos

Traffic monitoring and traffic analysis is much needed to ensure a modern and convenient traffic system. However, it is a very challenging task as the traffic condition is dynamic which makes it quite impossible to maintain the traffic through traditional way. Designing a smart traffic system is also inevitable for the big and busy cities. In this paper, we propose a vision based traffic monitoring system that will help to maintain the traffic system smartly. We also generate an analysis of the traffic for a certain period, which will be helpful to design a smart and feasible traffic system for a busy city. In the proposed method, we use Haar feature based Adaboost classifier to detect vehicles from a video. We also count the number of vehicles appeared in the video utilizing two virtual detection lines (VDL). Detecting and counting vehicles by proposed method will provide an easy and cost effective solution for fruitful and operative traffic monitoring system along with information to design an efficient traffic model

COVID-19 Pandemic: A Comparative Prediction Using Machine Learning

Coronavirus Disease 2019 or COVID-19 is an infectious disease which is declared as a pandemic by the World Health Organization (WHO) have a noxious effect on the entire human civilization. Each and every day the number of infected people is going higher and higher and so the death toll. Many of country Italy, UK, USA was affected badly, yet since the identification of the first case, after a certain number of days, the scenario of infection rate has been reduced significantly. However, a country like Bangladesh couldn't keep the infection rate down. A number of algorithms have been proposed to forecast the scenario in terms of the number of infection, recovery and death toll. Here, in this work, we present a comprehensive comparison based on Machine Learning to predict the outbreak of COVID-19 in Bangladesh. Among Several Machine Learning algorithms, here we used Polynomial Regression (PR) and Multilayer Perception (MLP) and Long Short Term Memory (LSTM) algorithm and epidemiological model Susceptible, Infected and Recovered (SIR), projected comparative outcomes

Molecular beam epitaxy of highly crystalline GeSnC using CBr4 at low temperatures

Tensile-strained pseudomorphic Ge1–x–ySnxCy was grown on GaAs substrates by molecular beam epitaxy using carbon tetrabromide (CBr4) at low temperatures (171–258 °C). High resolution x-ray diffraction reveals good crystallinity in all samples. Atomic force microscopy showed atomically smooth surfaces with a maximum roughness of 1.9 nm. The presence of the 530.5 cm−1 local vibrational mode of carbon in the Raman spectrum verifies substitutional C incorporation in Ge1–x–ySnxCy samples. X-ray photoelectron spectroscopy confirms carbon bonding with Sn and Ge without evidence of sp2 or sp3 carbon formation. The commonly observed Raman features corresponding to alternative carbon phases were not detected. Furthermore, no Sn droplets were visible in scanning electron microscopy, illustrating the synergy in C and Sn incorporation and the potential of Ge1–x–ySnxCy active regions for silicon-based lasers.The authors acknowledge support from the National Science Foundation under Grant Nos. DMR-1508646, CBET-1438608, and PREM DMR-2122041, the Center for Dynamics and Control of Materials is supported by the National Science Foundation under Award No. DMR-1720595, and additional support by the University of Texas at Austin.Center for Dynamics and Control of Material

Fluorophore-NanoLuc BRET Reporters Enable Sensitive In Vivo Optical Imaging and Flow Cytometry for Monitoring Tumorigenesis

Fluorescent proteins are widely used to study molecular and cellular events, yet this traditionally relies on delivery of excitation light, which can trigger autofluorescence, photoxicity, and photobleaching, impairing their use in vivo. Accordingly, chemiluminescent light sources such as those generated by luciferases have emerged, as they do not require excitation light. However, current luciferase reporters lack the brightness needed to visualize events in deep tissues. We report the creation of chimeric eGFP-NanoLuc (GpNLuc) and LSSmOrange-NanoLuc (OgNLuc) fusion reporter proteins coined LumiFluors, which combine the benefits of eGFP or LSSmOrange fluorescent proteins with the bright, glow-type bioluminescent light generated by an enhanced small luciferase subunit (NanoLuc) of the deep sea shrimp Oplophorus gracilirostris. The intramolecular bioluminescence resonance energy transfer (BRET) that occurs between NanoLuc and the fused fluorophore generates the brightest bioluminescent signal known to date, including improved intensity, sensitivity and durable spectral properties, thereby dramatically reducing image acquisition times and permitting highly sensitive in vivo imaging. Notably, the self-illuminating and bi-functional nature of these LumiFluor reporters enables greatly improved spatio-temporal monitoring of very small numbers of tumor cells via in vivo optical imaging and also allows the isolation and analyses of single cells by flow cytometry. Thus, LumiFluor reporters are inexpensive, robust, non-invasive tools that allow for markedly improved in vivo optical imaging of tumorigenic processes

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed