2 research outputs found
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
Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review
Jamun leaf diseases pose a significant threat to agricultural productivity,
negatively impacting both yield and quality in the jamun industry. The advent
of machine learning has opened up new avenues for tackling these diseases
effectively. Early detection and diagnosis are essential for successful crop
management. While no automated systems have yet been developed specifically for
jamun leaf disease detection, various automated systems have been implemented
for similar types of disease detection using image processing techniques. This
paper presents a comprehensive review of machine learning methodologies
employed for diagnosing plant leaf diseases through image classification, which
can be adapted for jamun leaf disease detection. It meticulously assesses the
strengths and limitations of various Vision Transformer models, including
Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT,
IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper
reviews models such as Dense Convolutional Network (DenseNet), Residual Neural
Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural
Network (CNN), and Locally Reversible Transformer. These machine-learning
models have been evaluated on various datasets, demonstrating their real-world
applicability. This review not only sheds light on current advancements in the
field but also provides valuable insights for future research directions in
machine learning-based jamun leaf disease detection and classification