United International University

Recent developments in IoT-enabled cloud computing and interactive applications have made researchers rethink how healthcare services are currently provided. The IoT-cloud-based systems facilitate remote monitoring and support for patients. However, in the existing area, much emphasis has not been given to making the healthcare systems green. So, in this paper, we present an integrated framework for green healthcare and use cutting-edge technology to make an interactive user interface. We have also ensured the system’s scalability and performance ratio. This system interface has been designed and developed for patients and doctors, where patients can send their healthcare data using wearable sensors, and doctors can receive those data in real-time. For data identification and analysis, we have adopted Hierarchical Clustering Algorithms. Finally, we have come up with a solution for how to make the interactive healthcare experience better for everyone.

The motivation of the study is to gauge the impact of financial development, FDI, Technological innovation, and good governance on environmental degradation in the Arab Nation for the period 1991–2019. Several techniques have implemented, including error correction-based cointegration, cross-sectional ARDL, Non-linear ARDL and Heterogeneous causality test for directional causality. The results of Slope of homogeneity, CSD and unit root test following CIPS and CADF, revealed that research variables are exposed with heterogeneity properties, cross-sectionally dependent, and all the variables become stationary after the first difference. The long-run cointegration between explained and explanatory variables established through error correction based cointegrating test. Referring to results derived from CS-ARDL, study exposed financial development has a detrimental effect on environmental sustainability, suggesting the intensification of CO2 emission and ecological instability. On the other hand, the role of FDI, GG, and TI exposed beneficiary in mitigating the environmental adversity. The asymmetric assessment revealed asymmetric association between explained and core explanatory variables which is valid in the long-run and short-run horizon. Finally, the casual association, study unveiled bidirectional causality between FDI, TI and ED [FDI←→ED; TI←→ED]. On the policy note, the study advocated that environmental improvement through financial channels should be efficiently monitored in the case of credit extension and incorporation with existing environmental policies.

Many fields have been affected by COVID-19, including education. The pandemic has prompted a change in education due to the requirement for social distancing. Campuses are now closed in many educational institutions across the globe, and teaching and learning are now conducted online. Internationalization has significantly slowed down. A mixed-method study was designed for this research, with the goal of ascertaining the impact of COVID-19 on Bangladeshi students enrolled in higher education during and after the pandemic. A questionnaire with 19 questions on a Google form was used to collect quantitative data using a 4-point Likert scale and was conducted on 100 students from different universities in the southern part of Bangladesh, such as Barisal University, Patuakhali Science and Technology University, and Bangabandhu Sheikh Mujibur Rahman Science and Technology University. For collecting qualitative data, six quasi-interviews were conducted. A statistical package for Social Science (SPSS) was used to analyze both quantitative and qualitative data. The quantitative results demonstrated that during the COVID-19 pandemic, pupils continuously received teaching and learning. The current study’s findings revealed a significant positive correlation between the COVID-19 pandemic and teaching, learning, and student achievement and a significant negative correlation between the COVID-19 pandemic and student goals. The study also revealed that the COVID-19 pandemic had a detrimental effect on students enrolled in higher education programs at the universities. The qualitative judgment showed that students faced many problems when joining classes, such as poor Internet connection and insufficient network and technological facilities, etc. Some students live in rural areas and have slow Internet speeds, which sometimes prevented them from joining class. The findings of the study can help policy makers in higher education to review and adopt a new policy in higher education in Bangladesh. It can also help education instructors in universities to develop a proper study plan for their students.

Poverty is the curse for sustainable and equitable development worldwide by detreating environmental sustainability, economic instability, and inequality. However, as a remedy for poverty reduction, researchers over the past decade have examined the key macro determinants and established positive associations, implying the contributory role in poverty reduction. The study explores the environmental, energy, education, and foreign direct investment (FDI) effects on poverty reduction in Morocco and Tunisia from 1991 to 2020. We employed autoregressive distributed lagged (ARDL) and nonlinear ARDL frameworks to document the explanatory variables’ elasticity on poverty reduction in both the long- and short-run horizons. According to linear assessment, the study documented that education, energy, and FDI support poverty reduction. At the same time, the cost of environmental degradation has a detrimental effect on poverty augmentation. Referring to asymmetric assessment, the study established a long-run asymmetric association between asymmetric shocks of education, FDI, and energy with poverty. For directional association, the study has implemented a casualty test with the Fourier TY causality test and revealed a bidirectional association between education and poverty, and energy and poverty. Moreover, the unidirectional casualty was unveiled between FDI and poverty, and poverty and environment.

In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly machine learning models, are successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due to the lack of transparency behind their behavior, users need more understanding of how these models make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex machine learning systems utilize less transparent algorithms, thereby exacerbating the problem. This survey analyzes the significance and evolution of explainable AI (XAI) research across various domains and applications. Throughout this study, a rich repository of explainability classifications and summaries has been developed, along with their applications and practical use cases. We believe this study will make it easier for researchers to understand all explainability methods and access their applications simultaneously.

Green energy from Solar PV is getting increased attention in the industries due to the falling price of solar panels in the world market. A grid-tied inverter is one of the major components in such a system, where the DC energy from PV is converted to AC and synchronized with the grid to obtain power sharing between the PV and the grid for the industrial drives. In this paper, a DC link has been proposed instead of an AC link for interconnection between the solar PV system and the grid to run those industrial drives. In most modern industrial applications, induction motors are driven by VVVF (Variable Voltage and Variable Frequency) inverters to achieve efficient speed control. The inverters commonly have a rectifier section at the front end that rectifies the input AC to DC and the DC is then used in PWM mode to generate the required voltage and frequency for the induction motor operating under variable speed and load conditions. Such an inverter can use both AC or DC as the input so long the supply voltage has the right value for the inverter to operate. In our proposition, we eliminate the grid-tied inverter and use a DC link, created from the rectified AC and the regular Solar PV, to obtain the power-sharing between the PV output and the grid. Using the DC link output directly to energize the VVVF inverter has an impact on the performance of the inverter. In the proposed system, the solar PV array is designed in such a way that the grid remains as the supplementary power source only to supplement any shortfall in the PV output due to variable sunshine conditions. The control circuit used in this novel technique is inexpensive, efficient, and simple in design when compared to the grid-tied inverters. The proposed system has been implemented at Niagara Textiles in Gazipur, Bangladesh. The experimental/practical results are presented to validate the basic concept. Around a 20% reduction in the cost of energy has been reported in this paper, with a more than 90% efficient system. This will definitely make solar PV energy more competitive with regular energy and attractive to industries for its simplicity.

The aim of the work is to analyze the socio-economic and healthcare aspects that arise in the contemporary COVID-19 situation from Bangladesh perspective. We elaborately discuss the successive COVID-19 occurrences in Bangladesh with consequential information. The components associated with the COVID-19 commencement and treatment policy with corresponding features and their consequences are patently delineated. The effect of troublesome issues related to the treatment is detailed with supporting real-time data. We elucidate the applications of modern technologies advancement in epidemiological aspects and their existent compatibility in Bangladesh. We statistically analyze the real-time data through figurative and tabular approaches. Some relevant measures of central tendency and dispersion are utilized to explore the data structure and its observable specifications. For a clear manifestation, Z − scores of the COVID-19 components are analyzed through the Box-Whisker plot. We have discovered that the gathered data exhibit features that are unsatisfactory for the normal distribution, are highly positively skewed, and are predominated by the earliest occurrences. Infections and deaths were initially lower than the global average, but they drastically rose in the first quarter of 2021 and persisted for the remainder of the year. Substantial preventive results were produced by the region-wisetime-worthy moves. In the fourth quarter of 2021, the infections and deaths noticeably decreased, and the number of recoveries was highly significant. In the middle of 2022, a lethal rise in infections was observed in Bangladesh and that was quickly stabilized, and the pandemic ingredients were under control. According to our assessment, some concluding remarks are made at the end of this work.

A significant breakthrough in organised retail in an emerging economy such as Bangladesh, coupled with growing competition among mall managers, necessitates determination of the factors that contribute to a satisfactory shopping experience and long-term patronage intentions. Therefore, this study aimed to explore the factors influencing the overall experience of mall shoppers, which, in turn, shapes their patronage behaviour. For this purpose, 284 respondents were surveyed using the convenience sampling technique. Structural equation modelling was employed to test the hypothesised model. Among the three independent factors analysed, entertainment and accessibility wielded significant influence on shoppers’ experience, while tenant mix exerted a statistically insignificant influence. Altogether, these three independent variables, along with shoppers’ experience, accounted for 67% of the total variance in patronage. It was plausible to conclude that managing entertainment and accessibility can result in a more pleasant shopping experience. Being the first of its kind, this study investigated the combined impact of accessibility, tenant mix, and entertainment on the overall shopping experience that shapes patronage behaviour. This study’s findings can help comprehend the dynamics of customer management in the retail market of an emerging economy.

Over the past decade, tourism’s contribution to economic Section progress has emerged as an alternative avenue for socio-economic development, especially in the productive economy with natural beauty. On the other hand, the potential effects of tourism on the environment have also been unveiled in the literature, along with macroeconomic misbehavior due to erratic environmental changes. However, the study’s impetus is to inspect the reaction of tourism contribution to Bangladesh’s economy from 1991–2019 with ecological sustainability, good governance, and financial inclusion in the empirical assessment. With the implementation of both linear and non-linear frameworks, the present study has explored the elasticities of core explanatory variables on explained variables; for directional causality, the novel Fourier Toda and Yamamoto causality test has been executed. According to the combined cointegration test, Bangladesh has a long-run association between environmental sustainability, good governance, financial inclusion, and tourism development. Inferring from long-run symmetric and asymmetric cointegration, the test statistics revealed statistically significant at a 1% level, suggesting the long-run relations in the established empirical model. Considering the linear autoregressive disoriented lagged, the study established a negative and statistically significant linkage between environmental sustainably and tourism contribution, suggesting that the excessive inflows of carbon emission that environmental degradation dwindles the progress of tourism contribution. Whereas a positive and statistically significant influence runs from good governance and financial inclusion to tourism development, the suggestion of easy access to financial services and effective institutional activities prompts tourism activities, especially in the long-run. The asymmetric investigation established non-linearity in the empirical model for the long and short-run. In terms of asymmetric coefficients, the study unveiled the positive and negative shocks of environmental sustainability exposed negatively and statistically significant. In contrast, the asymmetric shocks of financial inclusion and good governance established positive and statistically substantial Bangladesh tourism development in the long and short-run. The directional causality assessment revealed bidirectional causality running between explanatory variables to tourism development.

In this era of rapid technological advancement, every individual’s daily life has become a routine of sharing their perspectives, opinions, emotions, and experiences through social networking sites and platforms on the Internet. These viewpoints can be used to establish strategies that can improve efficiency in a variety of areas such as business, politics, research, and analysis. Sentiment analysis (SA) is used in natural language processing (NLP) to automatically monitor, analyze, and categorize individuals’ thoughts and opinions in order to acquire a sense of general sentiment. To date, a significant amount of research has been conducted on SA of English language with remarkable successes. Unfortunately, there has been relatively insufficient research in the field of SA with the Bangla language. Despite the fact that romanized Bangla has gained in popularity among Bangla speakers as a result of the recent surge in social networks, there is even less research on romanized Bangla text. Therefore, this research has concentrated on the analysis of sentiment for both Bangla raw and romanized texts. In this study, a corpus of romanized Bangla texts has been constructed from a raw Bangla sentiment corpus. Furthermore, both of these corpora have been tested for SA using the deep recurrent neural network with continuous bag of words and skip-gram word2vec word embeddings for both binary and multi-label classifications. Finally, this study concludes with the comparative results and analysis of SA of both forms of Bangla texts, where SA of romanized Bangla texts outperforms its raw form.

Te model was created to assist in the appropriate allocation of water to produce crops to optimize net proft through monthly reservoir operation. Te model maximizes net crop revenue and determines the type and size of the cultivated crop for each zone, taking into account monthly reservoir water availability. Te following factors constrain the optimization model: (1) monthly reservoir water availability; (2) monthly water demand and irrigated farmland for crops; (3) limited crop areas in each zone; (4) projected fnal storage; (5) proportional sharing rule (PSR) for each zone. Te linear programming (LP) algorithm is used to formulate the model, which is then solved using the general algebraic modeling system (GAMS). Te model is applied to Hali Dam and validated using two criteria: (1) baseline scenarios (non-PSR) and (2) PSR scenarios in which all zones must have the same amount of water. Te results demonstrate that the PSR scenarios give all of these zones identical rights for water delivery, with a total net proft reduction of around 2.6 percent at the planned fnal storage of 100 Hm 3. As a result, the current model can be utilised to optimize dam water consumption in the future. Te methodology is applied to a reservoir of Hali Dam in Saudi Arabia to demonstrate the model's practical application.

The unpredictable and crucial challenges that occurred because of the COVID-19 pandemic disease have taken a gradual upsurge impacting over 213 countries across the globe. Different countries have taken several measures to get control over it like Lockdown, Curfews, Travel ban, etc. but still the cases were increasing and the situation was getting worse globally during some period of time. The impacts on the financial, social, and physical aspects of several citizens resulted in their psychological and mental health issues. In this work, we have quantitatively analyzed the depression, stress, and suicide cases during the period of COVID-19 globally and especially, in India. The global data including tweets (collected using a Scraper) is used for analysis. The data have been analyzed on Tableau and; sentiment analysis for extracting emotions in tweets has been performed using Python. Tweets are analyzed to extract the emotion of people in terms of Fear, Sadness, Anger, and Happiness. With total collected Tweets of 819678 from Jan 2020 to March 2022, it is found that people are more into Fear and Sadness with 59.3% and 28.9% scores respectively.

In recent years, companies have been under increasing pressure from consumers, grassroots and community organizations, governments, and shareholders to develop and practice sustainable business practices. Academic and corporate interest in sustainable supply chain management has risen considerably in recent years. This can be seen in the number of papers published. This paper aims to systematically investigate the discipline of supply chain management (SCM) within the context of sustainability. The two concepts are increasingly aligned, and sustainable supply chain management (SSCM) represents an evolving field where they explicitly interact. The study proposes a conceptual framework to classify various factors along the triple bottom-line pillars of sustainability issues in the context of supply chains. The findings indicate that the existing literature is primarily focused on individual sustainability and supply chain dimensions rather than taking a more integrated approach. Also, the economic benefits of developing a sustainable supply chain for an organization are discussed in addition to specific features of sustainable supply chains and limitations of existing research; this should stimulate further research. Our analysis revealed trends and gaps, allowing us to create a solid agenda for additional SSCM research.

The lack of proper healthcare facilities, resource constraints, and a non-functional referral system hinder Bangladesh’s healthcare system offering comprehensive primary and preventive healthcare (PPH) services. To address these issues, cloud-based medical system framework (CMED) health created the digital general practitioner (GP) model for the rural people of Bangladesh with digital health account and structured referral mechanism. In this study, we introduce this digital GP model integrated with digital platforms to ensure PPH with referrals for rural Bangladesh. Overall, the digital GP model consists of applications for users, health workers, GP doctors, and management, for service delivery and monitoring. By utilizing this digital GP model, rural people can get regular doorstep health checkups, track their health conditions, take necessary steps to prevent diseases in early stages, reduce their out-of-pocket expenditure, and consult with GP doctors through telemedicine or physical visit. During the pilot project, this digital GP model served a total of 11,890 people, consisting of 4752 men (39.97%) and 7138 women (60.03%). From our data analysis, 438 (7.02%) adult people were suspected of having diabetes among 6239 blood sugar measurement, 164 (2.56%) adult people were found obese among 6383 BMI measurement, and 1991 (19.41%) adult people were suspected of hypertension among 10,260 blood pressure measurement. In addition, among young people, females were “underweight” at a higher percentage (72, or 22.71%) than males (52, or 20.72%) among 568 BMI measurement. Finally, the digital GP concept is a great method for the government to implement digital health inclusion and move closer to universal health coverage.

Cryptocurrencies acquire user confidence by making the whole creation and transaction history transparent to the public. In exchange, the transaction history accurately captures the complete range of user activities related to cryptocurrencies. It is thought to be one of the safest and simplest payment methods that may be employed in the future. The trend of banks and other financial institutions investing in cryptocurrencies has increased rapidly in recent years. Therefore, it is necessary to synthesize the findings of previous studies on cryptocurrencies. In this paper, the use of data mining methods in Bitcoin transactions is analyzed and summarized. Cryptocurrencies, similar to the well-known Bitcoin, were targeted to ensure transaction security and privacy and overcome the drawbacks of traditional banking systems as well as other centralized systems. In addition, a comprehensive analysis of the literature on the challenges and applications of electronic currencies is conducted. The evolution of digital currency from electronic cash to cryptocurrencies is summarized and the methods used to increase user privacy are highlighted. The security threats in existing cryptocurrency systems (that compromise the privacy of Bitcoin users) are also highlighted. Finally, several research gaps and trends are identified that need to be further explored.

Classification of Imbalance data is one of the most vital tasks in the field of machine learning because most of the real-life datasets available have an imbalanced distribution of class labels. The effect of imbalance data is severe where the predictive model trained on the imbalanced data faces some of the unprecedented problems like overfitting where the model gets biased towards the majority target class. Many techniques have been proposed over time to deal with the imbalanced distribution caused by problems like oversampling and undersampling where oversampling isn't able to match the performance acquired by the undersampling method. One such baseline method is clustering the majority data into multiple clusters and then randomly sampling some of the redundant data but we believe that randomly sampling the data sample might open the loophole to losing informative data samples. So, in this work, we would like to propose two clustering-based priority sampling methods which manage to boost the performance of the predictive model compared to the clustering-based random sampling techniques.

Introduction: The study’s motivation is to investigate the role of environmental and financial disclosure, IT adoption, and good governance on firms’ sustainability from 1990–2019. A sample of 75 financial institutions enlisted in Bangladesh’s capital market was considered for relevant data collection. Methodology: Secondary data sources were used for data accumulation, including annual reports of target FIs, economic review reports, and central banks publication. Several econometrical techniques have been implemented to document the empirical nexus and the elasticities of explained variables on firm performance. Findings: In terms of baseline assessment, the study revealed a positive and statistically significant association between a firm’s sustainability and target explanatory variables. Furthermore, the study extended the empirical valuation by implementing a system-GMM and documented a positive linkage between financial and environmental disclosure, IT adaptation, good governance, and the firm’s performance sustainability. Discussion: These study findings suggest that information symmetry, investor protection, and access to financial services foster and stabilize the firms’ performance. Concerning corporate governance’s mediating effect, the study established a mediating role with positive influences on financial performance augmentation. On the policy ground, the study postulated that financial policymakers should address fairness and integrity in disclosing information to the public. Enforcement has to be initiated to ensure good governance.