Browsing by Author "Acharjee, Uzzal Kumar"

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An SDN Based Distributed IoT Network with NFV Implementation for Smart Cities
(Springer, 2020-07-30) Mukherjee, Bivash Kanti; Pappu, Sadiqul Islam; Islam, Md. Jahidul; Acharjee, Uzzal Kumar
The Internet of Things (IoT) is an arrangement of connected numerous digital devices usually contained Unique Identifiers (UIDs) and have the capability to exchange data over a network without any human interaction. Another new paradigm Software-Defined Networking (SDN) comes in for the organization and control of the large amount of data produced by IoT devices. It separates the data plane from the control plane of network devices which enables easy configuration and management of those devices. Furthermore, Network Function Virtualization (NFV) is emerged to optimize and secure the SDN-IoT network. It enables network devices to be deployed as virtualized components via software. In this research, the authors have proposed an SDN based distributed IoT network with NFV implementation for smart cities. Where smart city is a residential area which utilizes Information and Communication Technology (ICT) as well as IoT network to develop the standard of living of its residents. The integration of NFV in the SDN-IoT network improves the network performance by increasing throughput, and time sequence while mitigating the round trip time as well. Moreover, the authors have used multiple distributed controllers and a clustering scheme to improve load balancing, scalability, availability, integrity, and security of the whole network.
Broadband multimedia satellite System: architecture, algorithm & implementations
(© University of Dhaka, 2025-05-27) Acharjee, Uzzal Kumar
DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images
(MDPI Publications, 2023-03-02) Ahamed, Md. Khabir Uddin; Islam, Md Manowarul; Uddin, Md. Ashraf; Akhter, Arnisha; Acharjee, Uzzal Kumar; Paul, Bikash Kumar; Moni, Mohammad Ali
COVID-19 is a severe respiratory contagious disease that has now spread all over the world. COVID-19 has terribly impacted public health, daily lives and the global economy. Although some developed countries have advanced well in detecting and bearing this coronavirus, most developing countries are having difficulty in detecting COVID-19 cases for the mass population. In many countries, there is a scarcity of COVID-19 testing kits and other resources due to the increasing rate of COVID-19 infections. Therefore, this deficit of testing resources and the increasing figure of daily cases encouraged us to improve a deep learning model to aid clinicians, radiologists and provide timely assistance to patients. In this article, an efficient deep learning-based model to detect COVID-19 cases that utilizes a chest X-ray images dataset has been proposed and investigated. The proposed model is developed based on ResNet50V2 architecture. The base architecture of ResNet50V2 is concatenated with six extra layers to make the model more robust and efficient. Finally, a Grad-CAM-based discriminative localization is used to readily interpret the detection of radiological images. Two datasets were gathered from different sources that are publicly available with class labels: normal, confirmed COVID-19, bacterial pneumonia and viral pneumonia cases. Our proposed model obtained a comprehensive accuracy of 99.51% for four-class cases (COVID-19/normal/bacterial pneumonia/viral pneumonia) on Dataset-2, 96.52% for the cases with three classes (normal/ COVID-19/bacterial pneumonia) and 99.13% for the cases with two classes (COVID-19/normal) on Dataset-1. The accuracy level of the proposed model might motivate radiologists to rapidly detect and diagnose COVID-19 cases.
Mechanistic Insight of Staphylococcus aureus Associated Skin Cancer in Humans by Santalum album Derived Phytochemicals
(Frontier Scientific Publishing, 2023-11-21) Hosen, Md. Eram; Supti, Sumaiya Jahan; Akash, Shopnil; Rahman, Md. Ekhtiar; Faruqe, Md Omar; Manirujjaman, M.; Acharjee, Uzzal Kumar; Gaafar, Abdel-Rhman Z.; Ouahmane, Lahcen; Sitotaw, Baye; Bourhia, Mohammed; Zaman, Rashed
An excessive amount of multidrug-resistant Staphylococcus aureus is commonly associated with actinic keratosis (AK) and squamous cell carcinoma (SCC) by secreted virulence products that induced the chronic inflammation leading to skin cancer which is regulated by staphylococcal accessory regulator (SarA). It is worth noting that there is currently no existing published study that reports on the inhibitory activity of phytochemicals derived from Santalum album on the SarA protein through in silico approach. Therefore, our study has been designed to find the potential inhibitors of S. aureus SarA protein from S. album-derived phytochemicals. The molecular docking study was performed targeting the SarA protein of S. aureus, and CID:5280441, CID:162350, and CID: 5281675 compounds showed the highest binding energy with −9.4 kcal/mol, −9.0 kcal/mol, and −8.6 kcal/mol respectively. Further, molecular dynamics simulation revealed that the docked complexes were relatively stable during the 100 ns simulation period whereas the MMPBSA binding free energy proposed that the ligands were sustained with their binding site. All three complexes were found to be similar in distribution with the apoprotein through PCA analysis indicating conformational stability throughout the MD simulation. Moreover, all three compounds’ ADMET profiles revealed positive results, and the AMES test did not show any toxicity whereas the pharmacophore study also indicates a closer match between the pharmacophore model and the compounds. After comprehensive in silico studies we evolved three best compounds, namely, Vitexin, Isovitexin, and Orientin, which were conducted in vitro assay for further confirmation of their inhibitory activity and results exhibited all of these compounds showed strong inhibitory activity against S. aureus. The overall result suggests that these compounds could be used as a natural lead to inhibit the pathogenesis of S. aureus and antibiotic therapy for S. aureus-associated skin cancer in humans as well.