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8. Verifi-Chain: A Credentials Verifier using Blockchain and IPFS

Author

Rahman, Tasfia, Mouno, Sumaiya Islam, Raatul, Arunangshu Mojumder, Azad, Abul Kalam Al, and Mansoor, Nafees

Subjects
Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Submitting fake certificates is a common problem in Southeast Asia, which prevents qualified candidates from getting the jobs they deserve. When applying for a job, students must provide academic credentials as proof of their qualifications, acquired both inside and outside the classroom. Verifying academic documents before hiring is crucial to prevent fraud. Employing blockchain technology has the potential to address this issue. Blockchain provides an electronic certificate that is tamper-proof and non-repudiable, making it difficult for students to manipulate their academic credentials. This paper presents a prototype for an academic credential verification model that leverages the security features of blockchain and IPFS (Interplanetary File System). Certificates are temporarily stored in a database before being transferred to IPFS, where a unique hash code is generated using a hashing algorithm. This hash code serves as the certificate's unique identity and is stored in the blockchain nodes. Companies can verify an applicant's credentials by searching for the applicant and accessing their already verified certificates. Utilizing IPFS as a middleman storage platform lowers the expenses of directly storing massive data on the blockchain. To sum it up, the proposed solution would make the process of certificate verification more efficient, secure, and cost-effective. It would save time and resources that would otherwise be used to manually verify certificates., Comment: Presented at International Conference on. Inventive Communication and Computational Technologies 2023

Published
2023

13. A deep learning approach for brain tumor detection using magnetic resonance imaging

Author

Nayan, Al-Akhir, Mozumder, Ahamad Nokib, Haque, Md. Rakibul, Sifat, Fahim Hossain, Mahmud, Khan Raqib, Azad, Abul Kalam Al, and Kibria, Muhammad Golam

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The growth of abnormal cells in the brain's tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient's survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient's life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors.

Published
2022
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24. Synthesis, Structure, and In Vitro Pharmacological Evaluation of some New Pyrimidine-2-Sulfonamide Derivatives and Their Molecular Docking Studies on Human Estrogen Receptor Alpha and CDK2/Cyclin Proteins

Author

Jaber, Qassim A. H., Shentaif, Ahmed Hassen, Almajidi, Mohammed, Ahmad, Iqrar, Patel, Harun, Azad, Abul Kalam, Alnasser, Sulaiman Mohammed, Alatawi, Hanan Ali, Menaa, Farid, Alfaifi, Sulaiman Y.M., Rahman, Mohammed M., Ali, Meser M., and Rao, S. J. Aditya

Published
2023
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36. A Machine Learning Approach for Early Detection of Fish Diseases by Analyzing Water Quality

Author

Nayan, Al-Akhir, Mozumder, Ahamad Nokib, Saha, Joyeta, Mahmud, Khan Raqib, Azad, Abul Kalam Al, and Kibria, Muhammad Golam

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps to mitigate the potential outbreak and thus to avert financial losses with apparent negative implications to the national economy. Typically, fish diseases are caused by viruses and bacteria; according to biochemical studies, the presence of certain bacteria and viruses may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N in water, resulting in the death of fishes. Besides, natural processes, e.g., photosynthesis, respiration, and decomposition, also contribute to the alteration of water quality that adversely affects fish health. Being motivated by the recent successes of machine learning techniques, a state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately. Thus, it helps to take preemptive steps against potential fish diseases. The experimental results show high accuracy in detecting fish diseases specific to water quality based on the algorithm with real datasets.

Published
2021
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37. Chitosan-based Mupirocin and Alkanna tinctoria extract nanoparticles for the management of burn wound: In vitro and in vivo characterization

Author

Alam Shah Muhammad Khurshid, Nawaz Asif, Latif Muhammad Shahid, Ullah Wasi, Ullah Aziz, Khan Azmat Ali, Malik Abdul, Kumarasamy Vinoth, Subramaniyan Vetriselvan, and Azad Abul Kalam

Subjects
mupirocin, alkanna tinctoria, chitosan, nanoparticles, burn wound, Technology, Chemical technology, TP1-1185, Physical and theoretical chemistry, QD450-801
Abstract

Serious consequences including septicemia and amputations can result from complex wounds, which is a serious healthcare concern. In addition, there are currently only a few choices for management, which justifies the search for novel, highly effective wound-healing medications. This research work was aimed at fabricating chitosan-based Alkanna tinctoria and Mupirocin nanoparticles by ionic gelation technique for burn wound management. Preliminary studies were conducted, and the prepared nanoparticles were characterized by various techniques that involve, high-performance liquid chromatography for the detection of components in A. tinctoria root extract, ATR-FTIR, particle size, zeta potential, percent drug content (DC%), percent entrapment efficiency (EE%), scanning electron microscopy, and transmission electron microscopy (TEM) for surface morphology. The optimized formulation CS-AT-MU-NPs3 shows a particle size of 340.8 ± 34.46 nm and positive zeta potential 27.3 ± 3.10 mV. In vitro drug release study was also performed, which demonstrated improved and controlled release of the drug from the nanoparticles. The CS-AT-MU-NPs3 exhibited a maximum release up to 92.61% (AT) and 88.35% (MU). Antibacterial and antifungal activities of the formulation were also accessed by utilizing the agar well diffusion technique. The combination of AT and MU in chitosan-based nanoparticles was significantly effective against bacterial and fungal strains like Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans as compared to other formulations. The skin irritation study was also conducted, which shows that the prepared formulation did not cause any observable changes to the skin in terms of inflammation, erythema, edema, or any other symptoms associated with skin irritation. All the chitosan-based nanoparticles showed almost 75% reduction in wound contraction, while the optimized formulation CS-AT-MU-NPs3 showed complete wound healing on the 15th day. Based on the results, it can be assumed that chitosan-based nanoparticles containing A. tinctoria and Mupirocin demonstrated good wound healing and could be used to effectively manage burn wounds of any description.

Published
2024
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41. Real Time Multi-Class Object Detection and Recognition Using Vision Augmentation Algorithm

Author

Nayan, Al-Akhir, Saha, Joyeta, Mozumder, Ahamad Nokib, Mahmud, Khan Raqib, and Azad, Abul Kalam Al

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

The aim of this research is to detect small objects with low resolution and noise. The existing real time object detection algorithm is based on the deep neural network of convolution need to perform multilevel convolution and pooling operations on the entire image to extract a deep semantic characteristic of the image. The detection models perform better for large objects. The features of existing models do not fully represent the essential features of small objects after repeated convolution operations. We have introduced a novel real time detection algorithm which employs upsampling and skip connection to extract multiscale features at different convolution levels in a learning task resulting a remarkable performance in detecting small objects. The detection precision of the model is shown to be higher and faster than that of the state-of-the-art models.

Published
2020

48. Contributors

Author

Alam, Tanvir, primary, Alasmael, Noura, additional, Alazmi, Meshari, additional, Almahasheer, Hanan, additional, Al-Talla, Zeyad A., additional, Al-Younis, Inas, additional, Azad, Abul Kalam, additional, Bastaki, Kholoud, additional, Biswas, Kabir H., additional, Bruginski, Estevan, additional, Dhahri, Manel, additional, Emwas, Abdul-Hamid, additional, Hakim, Al, additional, Jaremko, Łukasz, additional, Jaremko, Mariusz, additional, Jithesh, Puthen Veettil, additional, Lachowicz, Joanna Izabela, additional, Qureshi, Rizwan, additional, Rahman, Mahbuba, additional, Ramaswamy, Mamatha, additional, Sohag, Md. Mehadi Hasan, additional, Soufan, Othman, additional, Szczepski, Kacper, additional, and Umlai, Umm-Kulthum Ismail, additional

Published
2023
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49. Contributors

Author

Adhikari, Arpita, primary, Alanalp, Mine Begum, additional, Aydın, Elif Burcu, additional, Aydın, Muhammet, additional, Azad, Abul Kalam, additional, Baig, Mirza Shahed, additional, Balaji, D., additional, Bavage, Shyamlila B., additional, Benjamin, Stephen Rathinaraj, additional, Berrada, Mohammed, additional, Bhattacharya, Tanima, additional, Bhuvaneswari, V., additional, Camlibel, Nurhan Onar, additional, Chakraborty, Sarit, additional, Chalivendra, Vijaya, additional, Chattopadhyay, Dipankar, additional, Chaus, Wajid N., additional, Chopra, Hitesh, additional, Cochrane, Cedric, additional, Damiri, Fouad, additional, Das, Madhurima, additional, Das, Narayan Chandra, additional, Das, Poushali, additional, Das, Tushar Kanti, additional, De, Sriparna, additional, Dogu, Merve, additional, Durmus, Ali, additional, Dutra, Rosa Fireman, additional, Gaiji, Houda, additional, Ganguly, Sayan, additional, Gebretsadik, Abbay, additional, Gedanken, Aharon, additional, Ghatak, Avishek, additional, Ghosh, Prosenjit, additional, Ghosh, Suman Kumar, additional, Gupta, Akanksha, additional, Haftu, Mamu, additional, Jadhav, Prerana Badrinath, additional, Kaur, Damandeep, additional, Kaur, Manpreet, additional, Kaur, Navneet, additional, Khan, Shamim Ahmed, additional, Khan, Sharuk L., additional, Koncar, Vladan, additional, Kottiyatil, Abhilash J., additional, Lazim, Nurul Asmak Md, additional, Madhavan, K., additional, Majhi, Abhisek, additional, Majid, A.M.S. Abdul, additional, Maruthapandi, Moorthy, additional, Mozafari, M.R., additional, Muhamad, Ida Idayu, additional, Murthy, H.C. Ananda, additional, Nath, Jyotishka, additional, Nath, Pinku Chandra, additional, Naveen, V., additional, Nemera, Tesfaye, additional, Orasugh, Jonathan Tersur, additional, Parameswaranpillai, Jyotishkumar, additional, Paul, Pijush, additional, Porkodi, P., additional, Pramanik, Amit, additional, Rajabzadeh, Amin Reza, additional, Rajeshkumar, L., additional, Ramesh, M., additional, Ravikumar, C.R., additional, Roy, Biplab, additional, Saravanan, Arumugam, additional, Sengupta, Bratin, additional, Sezgintürk, Mustafa Kemal, additional, Siddiqui, Falak A., additional, Singh, Narinder, additional, Srinivasan, Seshasai, additional, Sultana, Rokeya, additional, Sunil, Sheeja, additional, and Vikhe, Sunayana Rahul, additional

Published
2023
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