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Your search keyword '"Aishi, Airin Afroj"' showing total 3 results
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3 results on '"Aishi, Airin Afroj"'

1. Blockchain-based AI Methods for Managing Industrial IoT: Recent Developments, Integration Challenges and Opportunities

Author

Rahman, Anichur, Kundu, Dipanjali, Debnath, Tanoy, Rahman, Muaz, Aishi, Airin Afroj, Islam, Jahidul, Rahman, Anichur, Kundu, Dipanjali, Debnath, Tanoy, Rahman, Muaz, Aishi, Airin Afroj, and Islam, Jahidul

Abstract

Currently, Blockchain (BC), Artificial Intelligence (AI), and smart Industrial Internet of Things (IIoT) are not only leading promising technologies in the world, but also these technologies facilitate the current society to develop the standard of living and make it easier for users. However, these technologies have been applied in various domains for different purposes. Then, these are successfully assisted in developing the desired system, such as-smart cities, homes, manufacturers, education, and industries. Moreover, these technologies need to consider various issues-security, privacy, confidentiality, scalability, and application challenges in diverse fields. In this context, with the increasing demand for these issues solutions, the authors present a comprehensive survey on the AI approaches with BC in the smart IIoT. Firstly, we focus on state-of-the-art overviews regarding AI, BC, and smart IoT applications. Then, we provide the benefits of integrating these technologies and discuss the established methods, tools, and strategies efficiently. Most importantly, we highlight the various issues--security, stability, scalability, and confidentiality and guide the way of addressing strategy and methods. Furthermore, the individual and collaborative benefits of applications have been discussed. Lastly, we are extensively concerned about the open research challenges and potential future guidelines based on BC-based AI approaches in the intelligent IIoT system.

Published
2024

2. Categorization of Dehydrated Food through Hybrid Deep Transfer Learning Techniques.

Author

Nobel, Sm. Nuruzzaman, Hussen Wadud, Md. Anwar, Rahman, Anichur, Kundu, Dipanjali, Aishi, Airin Afroj, Sazzad, Sadia, Rahman, Muaz, Imran, Md. Asif, Sifat, Omar Faruque, Sayduzzaman, Mohammad, and Ul Haque Bhuiyan, T. M. Amir

Subjects
DEEP learning, FOOD dehydration, FOOD quality, DRIED foods, TRANSFER of training
Abstract

The essentiality of categorizing dry foods plays a crucial role in maintaining quality control and ensuring food safety for human consumption. The effectiveness and precision of classification methods are vital for enhanced evaluation of food quality and streamlined logistics. To achieve this, we gathered a dataset of 11,500 samples from Mendeley and proceeded to employ various transfer learning models, including VGG16 and ResNet50. Additionally, we introduce a novel hybrid model, VGG16-ResNet, which combines the strengths of both architectures. Transfer learning involves utilizing knowledge acquired from one task or domain to enhance learning and performance in another. By fusing multiple Deep Learning techniques and transfer learning strategies, such as VGG16-ResNet50, we developed a robust model capable of accurately classifying a wide array of dry foods. The integration of Deep Learning (DL) and transfer learning techniques in the context of dry food classification signifies a drive towards automation and increased efficiency within the food industry. Notably, our approach achieved remarkable results, achieving a classification accuracy of 99.78% for various dry food images, even when dealing with limited training data for VGG16-ResNet50. [ABSTRACT FROM AUTHOR]

Published
2024
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