Influence of Word of Mouth on Consumer Buying Decision: Evidence from Bangladesh Market.

The purpose of this study is to define how word of mouth influence consumer’s buying behavior.  Word of mouth is becoming a strong tool for building brand in present time. The research used primary and secondary data for analysis. In primary data, 500 respondents’ data were collected and Microsoft excel used for analysis. The findings recommend that word of mouth has impact on consumer buying behavior. The results suggest that word of mouth built by trust and loyalty. The findings are based on small sample size however; the framework may be used for future research. The significance of word of mouth, particularly consumer buying behavior, is increased rapidly. The paper will give marketers a better understanding of word of mouth as well as consumer perceptions. Keywords: Word of mouth, Network marketing, Consumer buying behaviour, Consumer trust, Consumer loyalty

Mediating Effect of BMI on the Association of Economic Status and Coexistence of Hypertension and Diabetes in Bangladesh: A Counterfactual Framework-based Weighting Approach

Background and Aims Non-communicable diseases such as hypertension and diabetes are matters of huge concern worldwide, with an increasing trend in prevalence over the previous decade. First of all, this study aimed to evaluate the association between economic status (ES) and body mass index (BMI), ES and comorbidity of hypertension and diabetes, and BMI and comorbidity independently. Second, it explored the mediating role of BMI in the association between ES and comorbidity of hypertension and diabetes. Finally, it investigated whether the mediating effect differs with the place of residence, gender, and education levels. Methods A total of 11,291 complete cases from the Bangladesh demographic and health survey 2017–18 were utilized for this study. Survey-based binary logistic regression or multiple logistic regression was used to find the association among outcome, exposure, and mediator variables, and a counterfactual framework-based weighting approach was utilized for mediation analysis. Results Middle-income (adjusted odds ratio [AOR]: 1.696, 95% confidence interval [CI]: 1.219, 2.360) and rich (AOR: 2.770, CI: 2.054, 3.736) respondents were more likely to have comorbidity of hypertension and diabetes compared to the poor. The odds of comorbidity increased with the increase in BMI. A positive association was observed between ES and BMI. A significant mediating role of BMI in the association between ES and comorbidity was found. We observed that 19.85% (95% CI: 11.50%, 49.6%) and 20.35% (95% CI: 14.9%, 29.3%) of total effect was mediated by BMI for middle and rich respondents, respectively, compared to the poor. Conclusions The mediating role of BMI was greater for female, no or primary educated respondents, and respondents from rural areas. Therefore, the study will facilitate policymakers of Bangladesh and other countries with a similar set-up to decide on health policies regarding hypertension and diabetes

Implementation of Back Propagation Neural Network with PCA for Face Recognition

Face recognition is truly one of the demanding fields of biometric image processing system Within this paper we have implemented Back Propagation Neural Network for face recognition using MATLAB where feature extraction and face identification system completely depend on Principal Component Analysis PCA Face images are multidimensional and variable data Hence we cannot directly apply Back Propagation Neural Network to classify face without extracting the core area of face So the dimensionality of face image is reduced by the Principal Component Analysis algorithm then we have to explore unique feature for all stored database images called eigenfaces of eigenvectors These unique features or eigenvectors are given as parallel input to the Back Propagation Neural Network BPNN for recognition of given test images Here test image is taken from the integrated webcam which is applied to the BPNN trained network The maximum output of the tested network gives the index of recognized face image BPNN employing PCA is more robust and reliable than PCA based face recognition syste

Implementation and Performance Analysis of Different Hand Gesture Recognition Methods

In recent few years, hand gesture recognition is one of the advanced grooming technologies in the era of human-computer interaction and computer vision due to a wide area of application in the real world. But it is a very complicated task to recognize hand gesture easily due to gesture orientation, light condition, complex background, translation and scaling of gesture images. To remove this limitation, several research works have developed which is successfully decrease this complexity. However, the intention of this paper is proposed and compared four different hand gesture recognition system and apply some optimization technique on it which ridiculously increased the existing model accuracy and model running time. After employed the optimization tricks, the adjusted gesture recognition model accuracy was 93.21% and the run time was 224 seconds which was 2.14% and 248 seconds faster than an existing similar hand gesture recognition model. The overall achievement of this paper could be applied for smart home control, camera control, robot control, medical system, natural talk, and many other fields in computer vision and human-computer interaction

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

Brain Tumour Segmentation using Level Set Method and Affected Area Calculation

Medical image processing is the most important and challenging field now days. MRI image processing is one of the parts of this field. Brain tumour segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. In this paper we proposed a variational level set method and some morphological operation to segment the brain tumour from MRI image by using MATLAB. Actually we describe variational formulation on geometric active contours that forces the level set function at zero level to be close to signed distance function and without re-initialization process. The variational formulation uses energy function and partial diferential equation to evolve the level set function. Tumour shape area is connected component in binary image and calculated this connected area using some properties of morphological operation

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods