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Your search keyword '"Hossain, Md. Shakhawat"' showing total 389 results
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389 results on '"Hossain, Md. Shakhawat"'

2. AMDNet23: A combined deep Contour-based Convolutional Neural Network and Long Short Term Memory system to diagnose Age-related Macular Degeneration

Author

Ali, Md. Aiyub, Hossain, Md. Shakhawat, Hossain, Md. Kawar, Sikder, Subhadra Soumi, Khushbu, Sharun Akter, and Islam, Mirajul

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

In light of the expanding population, an automated framework of disease detection can assist doctors in the diagnosis of ocular diseases, yields accurate, stable, rapid outcomes, and improves the success rate of early detection. The work initially intended the enhancing the quality of fundus images by employing an adaptive contrast enhancement algorithm (CLAHE) and Gamma correction. In the preprocessing techniques, CLAHE elevates the local contrast of the fundus image and gamma correction increases the intensity of relevant features. This study operates on a AMDNet23 system of deep learning that combined the neural networks made up of convolutions (CNN) and short-term and long-term memory (LSTM) to automatically detect aged macular degeneration (AMD) disease from fundus ophthalmology. In this mechanism, CNN is utilized for extracting features and LSTM is utilized to detect the extracted features. The dataset of this research is collected from multiple sources and afterward applied quality assessment techniques, 2000 experimental fundus images encompass four distinct classes equitably. The proposed hybrid deep AMDNet23 model demonstrates to detection of AMD ocular disease and the experimental result achieved an accuracy 96.50%, specificity 99.32%, sensitivity 96.5%, and F1-score 96.49.0%. The system achieves state-of-the-art findings on fundus imagery datasets to diagnose AMD ocular disease and findings effectively potential of our method.

Published
2023

25. ICT-based solution for efficient fecal sludge management: An experience from Bangladesh

Author

Jakariya, Md., Nahla, Turasa, Ahmed, Silvia, Ishtiak, Taoseef, Islam, Md. Tahmidul, Alam, Md. Sajadul, Ali, Ahmed, Uddin Ruman, Md. Sharif, Saad, Saman, Bhattacharya, Prosun, Van der Voorn, Tom, Islam, Md. Aminul, Hossain, Md. Shakhawat, Amin, Kazi Al, Saha, Sumon Kumar, Rahman, Imamur, Adib, Hossain I., Mahzabin, Lamia, Murshed, Mohammed Fahim, Ahmed, Ragib, Jahan, Hasin, Ferdausi, Marufa, Barceló, Damià, and Sonne, Christian

Published
2023
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27. Traffic Analysis-Based Android SMS Malware Detection Using Machine Learning

Author

Mim, Kanis R., Hossain, Md. Sakir, Tisha, Sumona A., Kalpo, Kallul R., Bakul, Mehedi H., Hossain, Md. Shakhawat, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hossain, Sazzad, editor, Hossain, Md. Shahadat, editor, Kaiser, M. Shamim, editor, Majumder, Satya Prasad, editor, and Ray, Kanad, editor

Published
2022
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29. SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data.

Author

Wang, Juexin, Hossain, Md Shakhawat, Lyu, Zhen, Schmutz, Jeremy, Stacey, Gary, Xu, Dong, and Joshi, Trupti

Subjects
RNA‐seq, context‐specific network, database, interactome, soybean, context-specific network, RNA-seq
Abstract

The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA-Seq data generated in the project serve as a valuable resource for discovering tissue-specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context-specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context-specific networks at both transcriptome and interactome levels. BicMix and Cross-Conditions Cluster Detection algorithms were applied to detect modules based on co-expression relationships across all the tissues. Soybean context-specific interactomes were predicted by combining soybean tissue gene expression and protein-protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen-fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium-legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) at http://soykb.org/SoyCSN. SoyCSN provides a useful web-based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.

Published
2019

34. The integrated power of gastronomic experience quality and accommodation experience to build tourists' satisfaction, revisit intention, and word-of-mouth intention.

Author

Hossain, MD. Shakhawat, Hossain, MD. Alamgir, Al Masud, Abdullah, Islam, K. M. Zahidul, Mostafa, MD. Golam, and Hossain, Mir Tebrak

Abstract

The purpose of this study is to investigate a comprehensive multidimensional model of the gastronomic and accommodation experiences of domestic tourists, their links to satisfaction, trust, revisit, and word-of-mouth (WOM) intention in Bangladesh. Moreover, the present research examines the mediating impacts of tourists' satisfaction and trust by applying the stimulus-organism-response framework as its theoretical ground. An online survey was conducted to collect usable 365 cross-sectional data and is analyzed by structural equation modeling (SEM). Results confirm that gastronomic experience quality and accommodation experiences, accompanied by several sub-dimensions, have a significant impact on tourists' satisfaction, trust, and promote repeat visits and WOM intentions. Our results evidenced that satisfaction and trust play important mediating roles, although trust has a few exceptions. This study suggests that industry managers should adopt content marketing strategies and policies that consider various sub dimensions of the gastronomic and accommodation experiences to provide valuable and relevant experiences for psychometric perceptions of tourists, which in turn allows the tourism business to achieve long-term goals and ensuring sustainability in this sector. Tourists' perceptions of the quality of their gastronomic and accommodation experiences are offered as a tool for strategic experience design, and a model that explains their interactions with accommodation and gastronomy providers is provided. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Android Ransomware Detection From Traffic Analysis Using Metaheuristic Feature Selection

Author

Md. Sakir Hossain, Naim Hasan, Md. Abdus Samad, Hossain Md. Shakhawat, Joydeep Karmoker, Foysol Ahmed, K. F. M. Nafiz Fuad, and Kwonhue Choi

Subjects
Feature selection, malware, machine learning, particle swarm optimization, ransomware, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Among the prevalent cyberattacks on Android devices, a ransomware attack is the most common and damaging. Although there are many solutions for detecting Android ransomware attacks, existing solutions have limited detection accuracy and high computational complexity. This paper proposes a new Android ransomware detection method based on traffic analysis to address the limitations. We exploit particle swarm optimization (PSO) to select traffic characteristics. Then, based on the selected traffic features, we classify the data traffic using decision tree and random forest classifiers. We examine ransomware cyberattacks in two distinct circumstances. In the first case, we find ransomware traffic; in the second, we locate a specific form of malware traffic among benign traffic. The proposed PSO-assisted feature selection enables the classifier to improve the detection accuracy significantly. The random forest is found to achieve the highest performance in detecting ransomware, whereas the decision tree is the best for detecting the types of ransomware. The accuracy improvements are 2.26% and 3.7% in the first and second scenarios, respectively. The proposed method removes 56.01% to 91.95% of the features. The proposed method convergences quickly as the optimization reaches an optimum value of about ten iterations.

Published
2022
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36. Enhanced Antioxidant Activity and Secondary Metabolite Production in Tartary Buckwheat under Polyethylene Glycol (PEG)-Induced Drought Stress during Germination

Author

Hossain, Md. Shakhawat, primary, Li, Jing, additional, Wang, Chenyang, additional, Monshi, Fakhrul Islam, additional, Tabassum, Rehenuma, additional, Islam, Md Ashraful, additional, Faruquee, Muhiuddin, additional, Muktadir, Md. Abdul, additional, Mia, Md Sultan, additional, Islam, A. K. M. Mominul, additional, Hasan, Ahmed Khairul, additional, Sikdar, Ashim, additional, and Feng, Baili, additional

Published
2024
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47. Genetic determinants of SARS‐CoV‐2 and the clinical outcome of COVID‐19 in Southern Bangladesh

Author

Hasan, Md. Mahbub, primary, Saha, Chayan Kumar, additional, Hamidullah Mehedi, H. M., additional, Chakma, Kallyan, additional, Salauddin, Asma, additional, Hossain, Md. Shakhawat, additional, Sharmen, Farjana, additional, Rafiqul Islam, S. M., additional, Tanni, Afroza Akter, additional, Yasmin, Farhana, additional, Akash, Al‐Shahriar, additional, Hossain, Mohammad Enayet, additional, Miah, Mojnu, additional, Biswas, Sanjoy Kanti, additional, Sultana, Nahid, additional, and Mannan, Adnan, additional

Published
2024
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