Development and use of a new Speech Quality Evaluation Parameter ESNR using ANN and Grey Wolf Optimizer

197-200The performance of Speech Enhancement (SE) Algorithms is evaluated using various objective and subjective evaluation parameters. Recently, few objective evaluation parameters are developed for the measurement of speech quality and intelligibility. But still, there are ample scopes determining statistical parameters to predict the SNR of a noisy speech signal without using any reference of clean signal and noise. In this paper, this problem has been addressed and three types of Artificial Neural Networks (ANN) are developed for efficient prediction of the estimated SNR (E-SNR) of a given noisy speech signal. To further improve the accuracy of prediction of the SNR of the ANN, the coefficients of ANN are tuned using the bio-inspired optimization technique. In this paper, a popular and efficient Grey wolf Optimization is chosen for the purpose. Several audio features are studied and appropriate features are chosen as the inputs to the ANN. Finally, a comparative performance analysis is carried out using two standard speech databases and the best performing ANN and audio features are identified to provide the best ESNR

Towards the Exploration of Task and Workflow Scheduling Methods and Mechanisms in Cloud Computing Environment

Cloud computing sets a domain and application-specific distributed environment to distribute the services and resources among users. There are numerous heterogeneous VMs available in the environment to handle user requests. The user requests are defined with a specific deadline. The scheduling methods are defined to set up the order of request execution in the cloud environment. The scheduling methods in a cloud environment are divided into two main categories called Task and Workflow Scheduling. This paper, is a study of work performed on task and workflow scheduling. Various feature processing, constraints-restricted, and priority-driven methods are discussed in this research. The paper also discussed various optimization methods to improve scheduling performance and reliability in the cloud environment. Various constraints and performance parameters are discussed in this research

Architecture and Framework for Group Profiling System in Smart Homes

Smart homes are becoming a progressive reality in our society. Automation and customization are at the center of the functionality of smart homes. User profiles record the user preferences of the inhabitants. User profiles are the heart of smart home systems. Real-world smart homes have multiple residents in them. Most smart homes treat the gathering of users in the same area just as a collection of users, but in real-world scenarios, such a group has its own identity. The proposed system tackles this problem by introducing the notion of Group Profiling. This paper presents the significance of profiles and group profiles in a smart home to achieve better customization and automation

Mobile App Based Feature Extraction of a Speech Signal

Mobile phones are very much prevalent in today?s generation. They can be utilized in the diagnosis and treatment of many diseases. The traditional methods which are used for the diagnosis of the vocal cord disorder are usually invasive, expensive and slow. Sometimes, they are also annoying. So the purpose of this paper is to design a non-invasive technique for the feature extraction of speech signal which can later be used for the vocal cord disorder diagnosis which would be cheaper, faster and repeatable. This paper summarizes a study of the mobile app based technique used to extract features of a speech signal with an ultimate aim to discriminate and detect vocal cord disorder. The study is concentrated in the analysis of relevance of a set of features obtained from the analysis of phonated speech, specifically an open vowel as \a\. The features which are extracted for the mobile app are frequency, pitch, amplitude and jitter

Development and use of a new Speech Quality Evaluation Parameter ESNR using ANN and Grey Wolf Optimizer

The performance of Speech Enhancement (SE) Algorithms is evaluated using various objective and subjective evaluation parameters. Recently, few objective evaluation parameters are developed for the measurement of speech quality and intelligibility. But still, there are ample scopes determining statistical parameters to predict the SNR of a noisy speech signal without using any reference of clean signal and noise. In this paper, this problem has been addressed and three types of Artificial Neural Networks (ANN) are developed for efficient prediction of the estimated SNR (E-SNR) of a given noisy speech signal. To further improve the accuracy of prediction of the SNR of the ANN, the coefficients of ANN are tuned using the bio-inspired optimization technique. In this paper, a popular and efficient Grey wolf Optimization is chosen for the purpose. Several audio features are studied and appropriate features are chosen as the inputs to the ANN. Finally, a comparative performance analysis is carried out using two standard speech databases and the best performing ANN and audio features are identified to provide the best ESNR

EyeArt + EyePACS: Automated Retinal Image Analysis For Diabetic Retinopathy Screening in a Telemedicine System

Telemedicine frameworks are key to screening the large, ever-growing diabetic population for preventable blindness due to diabetic retinopathy (DR). Integrating fully-automated screening systems in telemedicine frameworks will make DR screening more efficient, cost-effective, reproducible, and accessible. In this paper, we present the integration of EyeArt, an automated DR screening system, into EyePACS, a telemedicine system for DR screening used in diverse screening settings. EyeArt in- corporates novel image processing and analysis algorithms for assessing image gradability; enhancing images based on median filtering; detecting interest regions and localizing lesions based on multi-scale morphological analysis; and DR screening and thus achieves robustness to the large image variability seen in a telemedicine system such as EyePACS. EyeArt is implemented as a scalable, high-throughput cloud-based system to enable large-scale DR screening. We evaluate the safety and performance of EyeArt on a dataset with 434,023 images from 54,324 patient cases obtained from EyePACS. On this dataset, EyeArt’s screening sensitivity is 90% at specificity 60.8% and the area under the receiver operating characteristic curve (AUROC) is 0.883. In a setup where trained human graders review patient cases recommended for referral by EyeArt with low confidence, a workload reduction of 62% is possible. Therefore, EyeArt can be safely integrated into large real world telemedicine DR screening programs such as EyePACS helping reduce workload and increase efficiency and thus help in reducing vision loss due to DR through early detection and treatment

A Molecular Docking and Pharmacokinetic Prediction of Thiazolidine-2, 4-dione Derivatives: Toward Novel Therapeutic Targets for Type-2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is a leading endocrine disorder that affects millions of people worldwide. It is characterized by hyperglycemia and high insulin resistance. The commonly prescribed oral therapeutic for insulin resistance in T2DM is Thiazolidine-2, 4-diones (TZDs). TZDs are a class of oral hypoglycemic agents that act on Peroxisome proliferator activating receptor-γ (PPAR-γ) receptors and are mainly expressed in the adipose tissues. In this work, we derive novel classes of TZDs and predict the nature of structural affinity using docking studies against the PPAR-γ.

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation