Securing Refugee Identity: A Literature Review on Blockchain-based Smart Contract

Identity documentation for refugees is a complex process and crucial for host nations. A secured identity management system ensures both security and the efficient provision of services for the host nation and the donor organizations. Realizing the benefits, a handful of studies enriched the blockchain-based security identification for refugees. The research studies presented the introductory, conceptual, and practical solution related to the blockchain-based smart contract. There is a common agreement in the studies that blockchain-based smart contract not only streamlines refugee identity verification but also safeguards against unauthorized entries. Since it is a technology as well, it has been essential to know the present status of the technology in the social context. In such a situation it becomes essential to review the existing research studies to provide insight for future studies. In this study, we reviewed current studies using a thematic approach. Our findings suggest researchers are more inclined to provide conceptual models as the models are important in advancing technology; however, the models need to be implemented for practical advances. However, the main contribution of this study is that this study gathers current efforts in smart contract-based refugee identity management. This study is important for the refugee host nations as well as for stakeholders. Knowledge gained from the study is expected to provide insight into how the technology can be developed using existing theory and implementation frameworks

Brain Cancer Segmentation Using YOLOv5 Deep Neural Network

An expansion of aberrant brain cells is referred to as a brain tumor. The brain's architecture is extremely intricate, with several regions controlling various nervous system processes. Any portion of the brain or skull can develop a brain tumor, including the brain's protective coating, the base of the skull, the brainstem, the sinuses, the nasal cavity, and many other places. Over the past ten years, numerous developments in the field of computer-aided brain tumor diagnosis have been made. Recently, instance segmentation has attracted a lot of interest in numerous computer vision applications. It seeks to assign various IDs to various scene objects, even if they are members of the same class. Typically, a two-stage pipeline is used to perform instance segmentation. This study shows brain cancer segmentation using YOLOv5. Yolo takes dataset as picture format and corresponding text file. You Only Look Once (YOLO) is a viral and widely used algorithm. YOLO is famous for its object recognition properties. You Only Look Once (YOLO) is a popular algorithm that has gone viral. YOLO is well known for its ability to identify objects. YOLO V2, V3, V4, and V5 are some of the YOLO latest versions that experts have published in recent years. Early brain tumor detection is one of the most important jobs that neurologists and radiologists have. However, it can be difficult and error-prone to manually identify and segment brain tumors from Magnetic Resonance Imaging (MRI) data. For making an early diagnosis of the condition, an automated brain tumor detection system is necessary. The model of the research paper has three classes. They are respectively Meningioma, Pituitary, Glioma. The results show that, our model achieves competitive accuracy, in terms of runtime usage of M2 10 core GPU

A Secured Model of IoT-based Smart Gas Detecting and Automatic Alarm System

A gas leakage detector is a device for detecting gases in an area that is often used in a security system. This type of equipment is used to detect gas leakage or another emission. A gas warning device can alert operators in the vicinity of a possible gas leak and enable them to escape. The device is important because many gases can be harmful to organic life, such as humans or animals. This can be used to detect flammable, flammable, and toxic gases, as well as a lack of oxygen. Identifying potentially dangerous gas leaks through sensors. These sensors often use an audible alarm to alert people when dangerous gas has been detected. The purpose of this paper is to propose and discuss the design of an IoT-based gas leakage detection system that can automatically detect and warn gas leaks. The proposed system also includes a warning system for users. The system is based on sensors that can easily detect gas leaks

A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia

Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people, especially in developing and impoverished countries where high levels of pollution, unclean living conditions, and overcrowding are frequently observed, along with insufficient medical infrastructure. Pleural effusion, a condition in which fluids fill the lung and complicate breathing, is brought on by pneumonia. Early detection of pneumonia is essential for ensuring curative care and boosting survival rates. The approach most usually used to diagnose pneumonia is chest X-ray imaging. The purpose of this work is to develop a method for the automatic diagnosis of bacterial and viral pneumonia in digital x-ray pictures. This article first presents the authors' technique, and then gives a comprehensive report on recent developments in the field of reliable diagnosis of pneumonia. In this study, here tuned a state-of-the-art deep convolutional neural network to classify plant diseases based on images and tested its performance. Deep learning architecture is compared empirically. VGG19, ResNet with 152v2, Resnext101, Seresnet152, Mobilenettv2, and DenseNet with 201 layers are among the architectures tested. Experiment data consists of two groups, sick and healthy X-ray pictures. To take appropriate action against plant diseases as soon as possible, rapid disease identification models are preferred. DenseNet201 has shown no overfitting or performance degradation in our experiments, and its accuracy tends to increase as the number of epochs increases. Further, DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time. This architecture outperforms the competition in terms of testing accuracy, scoring 95%. Each architecture was trained using Keras, using Theano as the backend.Comment: I have to remake the artical. Case there was some accuracy proble

Comparison of CNN-based deep learning architectures for rice diseases classification

Although convolutional neural network (CNN) paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures, few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice diseases. Moreover, most CNN-based rice disease detection studies only considered a small number of diseases in their experiments. Both these shortcomings were addressed in this study. In this study, a rice disease classification comparison of six CNN-based deep-learning architectures (DenseNet121, Inceptionv3, MobileNetV2, resNext101, Resnet152V, and Seresnext101) was conducted using a database of nine of the most epidemic rice diseases in Bangladesh. In addition, we applied a transfer learning approach to DenseNet121, MobileNetV2, Resnet152V, Seresnext101, and an ensemble model called DEX (Densenet121, EfficientNetB7, and Xception) to compare the six individual CNN networks, transfer learning, and ensemble techniques. The results suggest that the ensemble framework provides the best accuracy of 98%, and transfer learning can increase the accuracy by 17% from the results obtained by Seresnext101 in detecting and localizing rice leaf diseases. The high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural systems. This research is significant for farmers in rice-growing countries, as like many other plant diseases, rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality

Empowering RMG workers : towards a conceptual framework

In the Bangladeshi Ready-made Garment (RMG) industry, workers face constant hardships in communicating with government, accessing regulations and policies, banking to reduce poverty and disasters, educating themselves for a better life and accessing government services. Recently, scholars from developing countries have suggested that mobile phones have the potential to empower the deprived and disadvantaged. However, in the Bangladeshi context the topic is still under researched. This research fills a gap by presenting an initial mobile phone based empowerment framework for RMG workers. The framework examines possible mobile phone based empowerment tools that could provide workers with access to information, market and government. The framework suggests that outcomes include an increased sense of control, self-efficacy, knowledge and competency. In future, the empowerment framework presented in this research will be applied to the development of a mobile based information system for RMG workers. This research is significant as it adopts an empowerment focus rather than an individual consumer focused development outcomes. The outcomes and results of this research will be of potential value to the government, development agencies and mobile telecommunications in accelerating the development of mobile based services in Bangladesh and in other developing countries.7 page(s

Mobile phone enabled SCM : the Bangladeshi RMG sector

Relatively little is known about mobile phone use in a Supply Chain Management (SCM) context, especially in the Bangladeshi Ready-Made Garment (RMG) industry. RMG is a very important industry for the Bangladeshi economy, but is criticized for long product supply times due to poor SCM. RMG requires obtaining real-time information and enhanced dynamic control, through utilizing information sharing and connecting stakeholders in garment manufacture. However, a lack of IT support in the Bangladeshi RMG sector, the high price of computers and the low level of adoption of computer based internet are obstacles to providing sophisticated computer aided SCM. Alternatively, explosive adoption of mobile phones and continuous improvement of this technology is an opportunity to provide mobile based SCM for the RMG sector. This research presents a mobile phone based SCM framework for the Bangladeshi RMG sector. The proposed framework shows that mobile phone based SCM can positively impact communication, information exchange, information retrieval and flow, coordination and management, which represent the main processes of effective SCM. However, to capitalize on these benefits, it is also important to discover the critical success factors and barriers to mobile SCM systems.7 page(s

Towards a secured smart IoT using light weight blockchain: An aim to secure Pharmacy Products

Blockchain has proven a very developed and secured technology. It ensures data integrity with authentic connected nodes. Now-a-days, blockchain with IoT is a great combination for secured and smart end to end product delivery. This observation has motivated the research to develop a conceptual model to provide a secure pharmaceutical product delivery by developing a IoT integrated with lightweight blockchain. The undeveloped and most of the developing countries are facing problems such as drug counterfeits, shortages, opiates and tracking them became difficult because of less transparency. Also, nature sensitive medicines need to be stored under controlled temperature known as cold-chain shipping. The storage of these information in the recent software is done in the centralized databases that is prone to data manipulations and hacks. Due to less production drugs needed to be imported with maintaining drug supply chain regulations by law. This paper proposes a lightweight blockchain model for pharmaceutical industries by using IoT. This model ensures traceability of drugs within a very simple way which is less complex compared to the existing ones.Comment: 9 pages 3 figure