Conference Location: Sakheer, Bahrain

Authors: Habibullah, Md Oli Ullah, Minhaz Kamal, Md Sabbir Hasan Sohag and Md Saifur Rahman

DOI: 10.1109/3ICT60104.2023.10391458

Abstract: Transfer learning may boost modeling speed. This research unifies the improved VGG16 model. Skipping VGG16’s entirely connected layer and tying it to the layer following it improves CNN’s architecture and reduces its processing burden. CNNs are classifying cat and dog photos. With a small dataset, CNN training may take time and resources. Transfer learning uses ImageNet models to handle similar challenges in new contexts. Transfer learning enhances CNN cat-dog classification. The proposed technique selects a suitable pre-trained model, freezes its layers to retain acquired properties, adds layers to learn task-specific features, and tunes it using the cat and dog dataset. Data augmentation improves model performance and prevents overfitting. Hyperparameter optimization and validation enhance model accuracy and speed. This method enhances cat and dog photo classification even without adequate data to train CNNs with an accuracy of 97%.

Journal Location: Japan

Authors: Minhaz Kamal, Chowdhury Mohammad Abdullah, Fairuz Shaiara, Abu Raihan Mostofa Kamal, Md Mehedi Hasan, Jik-Soo Kim and Md Azam Hossain

DOI: 10.1587/transinf.2022EDL8099

Abstract: The literature presents a digitized pension system based on a consortium blockchain, with the aim of overcoming existing pension system challenges such as multiparty collaboration, manual intervention, high turnaround time, cost transparency, auditability, etc. In addition, the adoption of hyperledger fabric and the introduction of smart contracts aim to transform multi-organizational workflow into a synchronized, automated, modular, and error-free procedure.

Conference Location: Cox's Bazar, Bangladesh

Authors: Minhaz Kamal, Fairuz Shaiara, Chowdhury Mohammad Abdullah, Sabbir Ahmed, Tasnim Ahmed and Md Hasanul Kabir

DOI: 10.1109/ICCIT57492.2022.10054769

Abstract: Handwriting Recognition has been a field of great interest in the Artificial Intelligence domain. Due to its broad use cases in real life, research has been conducted widely on it. Prominent work has been done in this field focusing mainly on Latin characters. However, the domain of Arabic handwritten character recognition is still relatively unexplored. The inherent cursive nature of the Arabic characters and variations in writing styles across individuals makes the task even more challenging. We identified some probable reasons behind this and proposed a lightweight Convolutional Neural Network based architecture for recognizing Arabic characters and digits. The proposed pipeline consists of a total 18 layers containing four layers each for convolution, pooling, batch normalization, dropout, and finally one Global average pooling and a Dense layer. Furthermore, we thoroughly investigated the different choices of hyperparameters such as choice of optimizer, kernel initializer, activation function, etc. Evaluating the proposed architecture on the publicly available ‘Arabic Handwritten Character Dataset (AHCD)’ and ‘Modified Arabic handwritten digits Database (MadBase)’ datasets, the proposed model respectively achieved an accuracy of 96.93% and 99.35% which is comparable to the state-of-the-art and makes it a suitable solution for real-life end-level applications.

Conference Location: Cox's Bazar, Bangladesh

Authors: Chowdhury Mohammad Abdullah, Minhaz Kamal, Fairuz Shaiara, Abu Raihan Mostofa Kamal and Md Azam Hossain

DOI: 10.1109/ICCIT57492.2022.10055757

Abstract: Blood transfusion is an integral part of the healthcare system that plays an important role in ensuring the quality of care for patients undergoing a variety of medical procedures and treatments. A large portion of this blood comes from voluntary donors. The existing blood donor management systems are unable to offer a reliable audit trail and traceability. Hence, there is a significant risk that patients may get transfusion of blood from unreliable sources. In this paper, we propose a system built on Ethereum with the goal of creating a decentralized, transparent, traceable, and secure network of blood donors. The platform uses smart contracts to facilitate peer-to-peer interactions. To encourage donors to donate blood more regularly, the system also offers rewards in the form of tokens. Our source code is available in a public Github repository.