Md. Abul Kalam Azad

Bio: Md. Abul Kalam Azad is an academic researcher from Begum Rokeya University. The author has contributed to research in topics: Big data & Health informatics. The author has an hindex of 3, co-authored 4 publications receiving 190 citations.

Smart health

Abstract: Organized evaluation of various big data and smart system technology in healthcare context.Proposed a conceptual model on Big data enabled Smart Healthcare System Framework (BSHSF).We extract some depth information (some relevant examples) about advanced healthcare system.In depth study about state-of-the-art big data and smart healthcare system in parallel. In the era of big data, recent developments in the area of information and communication technologies (ICT) are facilitating organizations to innovate and grow. These technological developments and wide adaptation of ubiquitous computing enable numerous opportunities for government and companies to reconsider healthcare prospects. Therefore, big data and smart healthcare systems are independently attracting extensive attention from both academia and industry. The combination of both big data and smart systems can expedite the prospects of the healthcare industry. However, a thorough study of big data and smart systems together in the healthcare context is still absent from the existing literature. The key contributions of this article include an organized evaluation of various big data and smart system technologies and a critical analysis of the state-of-the-art advanced healthcare systems. We describe the three-dimensional structure of a paradigm shift. We also extract three broad technical branches (3T) contributing to the promotion of healthcare systems. More specifically, we propose a big data enabled smart healthcare system framework (BSHSF) that offers theoretical representations of an intra and inter organizational business model in the healthcare context. We also mention some examples reported in the literature, and then we contribute to pinpointing the potential opportunities and challenges of applying BSHSF to healthcare business environments. We also make five recommendations for effectively applying `BSHSF to the healthcare industry. To the best of our knowledge, this is the first in-depth study about state-of-the-art big data and smart healthcare systems in parallel. The managerial implication of this article is that organizations can use the findings of our critical analysis to reinforce their strategic arrangement of smart systems and big data in the healthcare context, and hence better leverage them for sustainable organizational invention.

Sentiment analysis for Bangla sentences using convolutional neural network

Abstract: Sentiment analysis, also known as opinion mining or emotion analysis, is a process to determine emotional reaction of people towards an interaction or an event. An opinion may be positive, negative or neutral depends on individuals judgment or evaluation towards a topic or an event. Usually sentiments can be varied in cultures and languages. On sentiment analysis, an extensive amount of research works can be seen for well-resourced languages like English, Japanese etc. However, such works are relatively less observed for low-resourced language like Bangla. In this paper, we propose a framework that analyzes sentiments from texts written in Bangla. In our proposal, we use Bangla comments and generate a classification model. The model is generated by a neural network variance called Convolutional Neural Network. The classifier model obtains a classification accuracy of 99.87%, which is 6.87% better than the available state-of-the art Bangla sentiment classifier.

Healthcare informatics and analytics in big data

Abstract: Healthcare informatics and analytics (HCI&A), also known as healthcare information technology (HIT), healthcare IS (HIS), and so on, has rapidly evolved with the emerge of advanced data analytics technologies applied to the medical domain. Currently, HCI&A has emerged as an important area of study for both practitioners and academic researchers. Accordingly, this emerging field has prompted for an inquiry of the opportunities and challenges related to management of healthcare data, and the application of advanced data analytics to the contemporary healthcare industry. In order to contribute to the literature of healthcare informatics and analytics, this study proposes an HCI&A framework under the context of big data, which covers four important segments such as the underlying technologies, system applications, system evaluations, and emerging research areas. Based on the key features and capabilities of underpinning technologies, the evolution of HCI&A are conceptualized by three stages, namely HCI&A 1.0, HCI&A 2.0, and HCI&A 3.0. By analyzing the technological growth and current research trends, this study outlines the trend map of HCI&A for education and knowledge transfer. We also contributed to conduct a bibliographic study on healthcare informatics and healthcare information systems. To the best of our knowledge, our study is among the very few comprehensive bibliographic studies about HCI&A. We hope that our study can contribute to supplement contemporary thoughts on HCI&A research, and facilitate the related knowledge transfer to the healthcare industry.

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Abstract: Thunderstorm frequency (TSF) prediction with higher accuracy is of great significance under climate extremes for reducing potential damages However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict To close this gap, we proposed two novel hybrid machine learning models through hybridization of data pre-processing ensemble empirical mode decomposition (EEMD) with two state-of-arts models, namely artificial neural network (ANN), support vector machine for TSF prediction at three categories over Bangladesh We have demarcated the yearly TSF datasets into three categories for the period 1981–2016 recorded at 28 sites; high (March–June), moderate (July–October), and low (November–February) TSF months The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared with classical ANN, SVM, and autoregressive integrated moving average EEMD-ANN and EEMD-SVM hybrid models showed 802–2248% higher performance precision in terms of root mean square error compared to other models at high-, moderate-, and low-frequency categories Eleven out of 21 input parameters were selected based on the random forest variable importance analysis The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category, and thunderstorm days are the most contributing parameters influencing TSF prediction The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision The hybrid models developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world

Future Trends and Current State of Smart City Concepts: A Survey

Abstract: Intelligent systems are wanting for cities to cope with limited spaces and resources across the world. As a result, smart cities emerged mainly as a result of highly innovative ICT industries and markets, and additionally, they have started to use novel solutions taking advantage of the Internet of Things (IoT), big data and cloud computing technologies to establish a profound connection between each component and layer of a city. Several key technologies congregate to build a working smart city considering human requirements. Even though the smart city concept is an advanced solution for today’s cities, recently, more living spaces should be discovered, and the concept of a smart city could be moved to these alternative living spaces, namely floating cities. The concept of a floating city emerged as a novel solution due to rising sea levels and land scarcity in order to provide alternative living spaces for humanity. In this article, our main research question is to raise awareness on the current state of smart city concepts across the world by understanding the key future trends, including floating cities, by motivating researchers and scientists through new IoT technologies and applications. Therefore, we present a survey of smart city initiatives and analyze their key concepts and different data management techniques. We performed a detailed literature survey and review by applying a complex literature matrix including terms, like smart people, smart economy, smart governance, smart mobility, smart environment, and smart living. We also discuss multiple perspectives of smart floating cities in detail. With the proposed approach, recent advances and practical future opportunities for smart cities can be revealed.