A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19.Comment: 18 pages, 2 figures, 4 Table

THE IMPACT OF ONE BELT ONE ROAD IN THE GARMENT SECTOR OF BANGLADESH

As the second largest garments producer and exporter in the world market after China, Bangladesh‘s readymade garment industry is one of the most important sector among others sectors, which serves a big market all over the world. Ready-made garments (RMG) of Bangladesh are considered as the backbone of its economy and have a great prospect to become the leading garment producer and exporter in the world market near future. Considering the biggest initiative of OBOR by Chinese government also known as New Silk Road initiative, which will be connected with more than 60 countries and sixty percent of total population in the world, Bangladesh is considered as a very important land both geographically and economically and has already declared to join in this initiative. While in recent years, the production cost of apparel in china is increasing very high and the opportunity cost of garments production is much higher than Bangladesh. So many buyers and producers are looking for a new destination for their investments and apparel business. In this situation OBOR initiative could be a window of opportunity as foreign direct investment (FDI) especially for RMG industry of Bangladesh. This paper focuses to find out what will be the impact of OBOR initiative on Bangladesh‘s economy specifically on garment sectors, and how Bangladesh will be benefited from this project, beside what steps Bangladesh government should be taken to make it meaningful.  Article visualizations

Isolation and Identification of Some Pathogenic Fungi from Selected Infected Vegetable Plants in Kushtia Local Area, Bangladesh

The aim of this study is to isolate the pathogenic fungi from selected infected vegetable plants in the local area of kushtia, Bangladesh. Some of the most devastating and harmful pathogenic fungi are Phytophthora infestans and Aspergillus nigerwhich we were isolated from edible vegetables potato and onion respectively by using Potato dextrose agar (PDA) using morphological and fungal characteristics. After the morphological study, we observe that Phytophthora infestans have Sporangia shape spores with the white color colony and aspergillus niger has produced black colonies with wooly smooth-walled colonies of conidia. Those fungi create tremendous yield loss subsequently decrease the market value and nutritional value of vegetables. These observed characteristics have similarities with the previous research of another scientist. But further study is needed to confirm our identified isolates. Because of the Covid-19 pandemic, we are unable to study more in the laboratory

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19

Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field.Comment: 32 pages, 4 figures, 4 Table

Coronary artery heart disease prediction: A comparative study of computational intelligence techniques

Diseases is an unusual circumstance that affects single or more parts of a human’s body. Because of lifestyle and patrimonial, different kinds of disease are increasing day by day. Among all those diseases, heart disease turns out to be the most common disease and the impact of this ailment is dangerous than all other diseases. In this paper, we compared a number of computational intelligence techniques for the prediction of coronary artery heart disease. Seven computational intelligence techniques named as Logistic Regression (LR), Support Vector Machine (SVM), Deep Neural Network (DNN), Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbor (K-NN) were applied and a comparative study was drawn. The performance of each technique was evaluated using Statlog and Cleveland heart disease dataset which are retrieved from the UCI machine learning repository database with several evaluation techniques. From the study, it can be carried out that the highest accuracy of 98.15% obtained by deep neural network with sensitivity and precision 98.67% and 98.01% respectively. The outcomes of the study were compared with the outcomes of the state of the art focusing on heart disease prediction that outperforms the previous study

Working Speed Analysis of the Gear-Driven Dibbling Mechanism of a 2.6 kW Walking-Type Automatic Pepper Transplanter

The development of an automatic walking-type pepper transplanter could be effective in improving the mechanization rate in pepper cultivation, where the dibbling mechanism plays a vital role and determines planting performance and efficiency. The objective of this research was to determine a suitable working speed for a gear-driven dibbling mechanism appropriate for a pepper transplanter, while considering agronomic transplanting requirements. The proposed dibbling mechanism consisted of two dibbling hoppers that simultaneously collected free-falling seedlings from the supply mechanism and dibbled them into soil. To enable the smooth collection and plantation of pepper seedlings, analysis was carried out via a mathematical working trajectory model of the dibbling mechanism, virtual prototype simulation, and validation tests, using a physical prototype. In the mathematical model analysis and simulation, a 300 mm/s forward speed of the transplanter and a 60 rpm rotational speed of the dibbling mechanism were preferable in terms of seedling uprightness and low mulch film damage. During the field test, transplanting was conducted at a 40 mm planting depth, using different forward speed levels. Seedlings were freely supplied to the hopper from a distance of 80 mm, and the success rate for deposition was 96.79%. A forward speed of 300 mm/s with transplanting speed of 120 seedlings/min was preferable in terms of achieving a high degree of seedling uprightness (90 ± 3.26), a low rate of misplanting (8.19%), a low damage area on mulch film (2341.95 ± 2.89 mm2), high uniformity of planting depth (39.74 ± 0.48 mm), and low power consumption (40.91 ± 0.97 W)