Adaptability of deep learning: datasets and strategies in fruit classification

This review aims to uncover the multifaceted landscape of methodologies employed by researchers for accurate fruit classification. The exploration encompasses an array of techniques and models, each tailored to address the nuanced challenges presented by fruit classification tasks. From convolutional neural networks (CNNs) to recurrent neural networks (RNNs), and transfer learning to ensemble methods, the spectrum of approaches underscores the innovative strategies harnessed to achieve precision in fruit categorization. A significant facet of this review lies in the analysis of the various datasets utilized by researchers for fruit classification. Different datasets present unique challenges and opportunities, thereby shaping the design and effectiveness of the models. From widely recognized datasets like Fruits-360 to specialized collections, the review navigates through a plethora of data sources, elucidating how these datasets contribute to the diversity of research endeavors. This insight not only highlights the variety in fruit types and attributes but also emphasizes the adaptability of deep learning techniques to accommodate these variations. By amalgamating findings from diverse articles, this study offers an enriched understanding of the evolving trends and advancements within the domain of fruit classification using deep learning. The synthesis of methodologies and dataset variations serves to inform future research pursuits, aiding in the refinement of accurate and robust fruit classification methods. As the field progresses, this review stands as a valuable compass, guiding researchers toward impactful contributions that enhance the accuracy and applicability of fruit classification models

Blockchain-based electronic health record system for efficient Covid-19 pandemic management

Electronic Health Record (EHR) is being used in most healthcare institutions to preserve and share health records instead of a paper-based method. Data records must be exchanged amongst various parties and users' privileges to manage access to their records should also be provided. In addition to the basic standards of secrecy, confidentiality and integrity of information, these facts further demonstrate the need for interoperability and consumer control to access their personal data. Electronic Health Record (EHR) system faces issues of protection of data, trust and management issues. In recent Covid-19 pandemic, various applications, tools and websites were launched that stores records. Also, many personal records related to health need to be shared among different parties for early detection, contact tracing, monitoring and the future prediction that requires accurate and reliable data. Simultaneously, the citizens will be hesitant in providing their personal details due to privacy concerns and social stigma. Blockchain technology has arisen as a powerful technology that can offer the immutability, confidentiality and user access properties of stored information and provided distributed storage. This paper analyses the blockchain suitability in EHR and its further applications in efficient Covid-19 pandemic management

Generating Image Captions Using Bahdanau Attention Mechanism and Transfer Learning

Automatic image caption prediction is a challenging task in natural language processing. Most of the researchers have used the convolutional neural network as an encoder and decoder. However, an accurate image caption prediction requires a model to understand the semantic relationship that exists between the various objects present in an image. The attention mechanism performs a linear combination of encoder and decoder states. It emphasizes the semantic information present in the caption with the visual information present in an image. In this paper, we incorporated the Bahdanau attention mechanism with two pre-trained convolutional neural networks—Vector Geometry Group and InceptionV3—to predict the captions of a given image. The two pre-trained models are used as encoders and the Recurrent neural network is used as a decoder. With the help of the attention mechanism, the two encoders are able to provide semantic context information to the decoder and achieve a bilingual evaluation understudy score of 62.5. Our main goal is to compare the performance of the two pre-trained models incorporated with the Bahdanau attention mechanism on the same dataset

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets

An extended review on internet of things (IoT) and its characterisation

The Internet of Things (IoT) Physical objects (or Groups of such materials) sensors, processing Skills, software and the Internet or other Communication connects with other devices and systems and other in exchange via networks Described as technologies. Invalid due to Internet of Things Devices Considered by name, they are associated with the public Internet. No need to connect, just the network Should only be connected and can be addressed individually. The ability to provide Machinery and digital machines, Objects, animals or personal Identifiers (UIDs) and from man to man or without the need for man-to-computer Data transfer over a network communication. In the last few years, IoT too it is too much 21st century Has become one of the most important technologies. Nowadays, everyday items combine Kitchen appliances, cars, Thermostats, baby screens through devices embedded on the Internet, making seamless communication between people, processes and objects possible. With Low-cost computing, cloud, big data, analytics and mobile technologies, physics minimal human intervention data share and let’s collect. In this high-connected world, digital Between system connected objects collaborate

DataSheet_1_Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach.zip

The irrigation sector in the Kingdom of Saudi Arabia (KSA) confronts a range of obstacles, such as scarce water resources, the elevated salinity and alkalinity of irrigation water, inefficient irrigation practices, and inter-sectoral competition for water resources. These challenges have led to diminishing agricultural yields and abandonment of arable lands. Internet of Things (IoT)-based irrigation systems present a promising remedy for these issues. By curbing water wastage and ensuring precise water delivery to crops, IoT-based irrigation systems offer a viable solution to the challenges entrenched in traditional irrigation methodologies in KSA. However, the widespread implementation of an IoT-based Smart Irrigation System (I-SIMS) poses a multifaceted and intricate challenge in KSA. This study is focused on the identification of the factors and challenges through a systematic review and ranking of the challenges/factors that exert a significant influence on the adoption of I-SIMS. Ranking aids in determining the importance of various alternatives. It enables locating the best options that support the required objectives in complex decision situations. The study employs both Grey Relational Analysis (GRA) and Analytical Hierarchical Process (AHP) methodologies to prioritize these factors. The study’s conclusive findings indicate that among the challenges, technical expertise and security measures emerge as the foremost concerns that demand attention.</p

Blockchain-Based Framework for Interoperable Electronic Health Records for an Improved Healthcare System

The healthcare industry has been transitioning from paper-based medical records to electronic health records (EHRs) in most healthcare facilities. However, the current EHR frameworks face challenges in secure data storage, credibility, and management. Interoperability and user control of personal data are also significant concerns in the healthcare sector. Although block chain technology has emerged as a powerful solution that can offer the properties of immutability, security, and user control on stored records, its potential application in EHR frameworks is not yet fully understood. To address this gap in knowledge, this research aims to provide an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards such as HIPAA and HL7. The research method employed is a systematic literature review to explore the current state of the art in the field of EHRs, including blockchain-based implementations of EHRs. The study defines the interoperability issues in the existing blockchain-based EHR frameworks, reviews various national and international standards of EHR, and further defines the interoperability requirements based on these standards. The proposed framework can offer safer methods to interchange health information for the healthcare sector and can provide the properties of immutability, security, and user control on stored records without the need for centralized storage. The contributions of this work include enhancing the understanding of the potential application of blockchain technology in EHR frameworks and proposing an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards. Overall, this study has significant implications for the healthcare sector, as it can enhance the secure sharing and storage of electronic health data while ensuring the confidentiality, privacy, and integrity of medical records