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6. Threat Modelling in Virtual Assistant Hub Devices

Author

LeClair, Beckett, Fatema, Kaniz, 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, Naik, Nitin, editor, Jenkins, Paul, editor, Prajapat, Shaligram, editor, and Grace, Paul, editor

Published
2024
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26. Deep Learning-Based Lentil Leaf Disease Classification

Author

Fatema, Kaniz, Rony, Md. Awlad Hossen, Puspita, Kazi Mumtahina, Hasan, Md. Zahid, Uddin, Mohammad Shorif, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Uddin, Mohammad Shorif, editor, and Jamwal, Prashant Kumar, editor

Published
2022
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27. Developing a System for Automatic Detection of Books

Author

Fatema, Kaniz, Ahmed, Md. Razu, Arefin, Mohammad Shamsul, 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, Chen, Joy Iong-Zong, editor, Tavares, João Manuel R. S., editor, Iliyasu, Abdullah M., editor, and Du, Ke-Lin, editor

Published
2022
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32. Plant growth‐promoting rhizobacteria mediate soil hydro‐physical properties: An investigation with Bacillus subtilis and its mutants

Author

Fatema Kaniz, Wenjuan Zheng, Harsh Bais, and Yan Jin

Subjects
Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract

Abstract Plant growth‐promoting rhizobacteria and other soil bacteria have the potential to improve soil hydro‐physical properties and processes through the production of extracellular polymeric substances (EPS). However, the mechanisms by which EPS mediates changes in soil properties and processes remain incompletely understood, partly due to variations in EPS composition produced under different environmental conditions. In this study, we investigated the influence of different bacterial traits on intrinsic soil properties and processes of evaporation and infiltration using sand treated with the wild‐type Bacillus subtilis variant (UD1022) and its two mutant variants, eps−–tasA− and srfAC−. The eps−–tasA− mutant suppresses EPS production through alterations in the eps and tasA genes, while the srfAC− mutant lacks the gene for surfactin production. Experimental results confirmed that the solution viscosity of the eps−–tasA− mutant was the lowest and the solution surface tension of the srfAC− mutant was the highest among the three tested bacteria strains. The distinct intrinsic properties of EPS produced by these bacterial strains resulted in varied hydro‐physical responses in the treated sand. Key influences included modifications in wettability, hydraulic decoupling (or mixed wettability), and aggregation, which collectively led to reduced evaporation rates and heterogeneous water distribution during infiltration in the bacteria‐treated sands. Our findings advance the understanding of the role bacterial EPS play in vadose zone hydrology and offer insights for the development of sustainable strategies for increasing water retention, supporting crop production in arid regions, and facilitating land restoration.

Published
2023
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34. A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted Research.

Author

Karim, Md. Rajaul, Syeed, M. M. Mahbubul, Rahman, Ashifur, Ayaz Rabbani, Khondkar, Fatema, Kaniz, Khan, Razib Hayat, Hossain, Md Shakhawat, and Uddin, Mohammad Faisal

Abstract

Assessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale. [ABSTRACT FROM AUTHOR]

Published
2025
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38. Trustworthy Cloud Computing

Author

Alofe, Olasunkanmi Matthew, Fatema, Kaniz, Lynn, Theo, Series Editor, Mooney, John G., Series Editor, van der Werff, Lisa, editor, and Fox, Grace, editor

Published
2021
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43. Analysing e-government service adoption in Bangladesh: a study on the Surokkha Vaccination Program

Author

Pathak, Koushik Prashad, Jamshed, Kazi Md., Arjumond, Shusama, Akter, Khadija, and Fatema, Kaniz

Abstract

The Government of Bangladesh has been undergoing a massive transformation in providing e-government services to its citizens and businesses since 2009. Although e-government services do hold promising merits, the adoption of e-government services is not that significant in Bangladesh. This study aims to identify the factors which drive end-users to adopt e-government services. In this study, the Surokkha vaccine service has been taken as a sample e-government service. Drawing on the extended UMEGA model, this study uses the structured equation modelling (SEM) approach through PLS-SEM to explore the behavioural intention of the end users. The findings revealed that effort expectancy, facilitation conditions, and perceived service quality have a significant influence on behavioural intention to use. Also, there are no such differences between male and female groups while adopting e-government services except for effort expectancy and facilitation condition variables. Several practical implications have been recommended to policymakers for making more user-driven e-government services.

Published
2025
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48. Contributors

Author

Barbé, Ferran, primary, Barney, Ryan C., additional, Barrott, Jared, additional, Beltrán-García, Jesús, additional, Berenguer-Pascual, Ester, additional, Cardona-Monzonís, Alejandro, additional, Carretero, Julian, additional, Cervantes, Andrés, additional, Chen, Lin-Yu, additional, Cosín-Roger, Jesús, additional, Crujeiras, Ana B., additional, de Gonzalo-Calvo, David, additional, Deml, Moritz C., additional, Fatema, Kaniz, additional, Ferrer-Buitrago, Minerva, additional, Friedland, Matthew H., additional, Gallagher, William M., additional, García-Giménez, José Luis, additional, Guerra, José M., additional, Hermida, Teresa Bas, additional, Heyneman, Alexandra L., additional, Hitchins, Megan P., additional, Huang, Rui-Lan, additional, Ibáñez-Cabellos, José Santiago, additional, Izquierdo, Andrea G., additional, Jenkins, Timothy G., additional, Jordan, Asia C., additional, Kim, Hong Sun, additional, Kirkham, Matthew, additional, Lai, Hung-Cheng, additional, Lambrou, George I., additional, Laurent-Puig, Pierre, additional, Liew, Phui-Ly, additional, Llorente-Cortés, Vicenta, additional, Lorenzo, Paula M., additional, Luelling, Sarah, additional, Maciel, Valter Luiz, additional, McGee, Jean S., additional, Meeran, Syed Musthapa, additional, Mena-Mollá, Salvador, additional, Mondal, Priya, additional, Natesh, Jagadish, additional, Ortega, Ángel L., additional, Osca-Verdegal, Rebeca, additional, Pallardó, Federico V., additional, Pavek, Adriene, additional, Peiró-Chova, Lorena, additional, Penta, Dhanamjai, additional, Pérez-Machado, Gisselle, additional, Perry, A.S., additional, Pinilla, Lucía, additional, Prencipe, M., additional, Ruiz-Jorro, Miguel, additional, Seco-Cervera, Marta, additional, Shi, Jiaqi, additional, Silva, Romina, additional, Su, Po-Hsuan, additional, Tollefsbol, Trygve O., additional, Tost, Jörg, additional, Ushijima, Toshikazu, additional, Wen, Kuo-Chang, additional, and Yi, Julie Z., additional

Published
2022
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49. An Automated Histopathological Colorectal Cancer Multi‐Class Classification System Based on Optimal Image Processing and Prominent Features.

Author

Tonni, Tasnim Jahan, Rana, Shakil, Fatema, Kaniz, Karim, Asif, Rony, Md. Awlad Hossen, Hasan, Md. Zahid, Mukta, Md. Saddam Hossain, and Azam, Sami

Subjects
FEATURE selection, COLON cancer, COLORECTAL cancer, COLON polyps, MACHINE learning, FEATURE extraction
Abstract

Colorectal cancer (CRC) is characterized by the uncontrollable growth of cancerous cells within the rectal mucosa. In contrast, colon polyps, precancerous growths, can develop into colon cancer, causing symptoms like rectal bleeding, abdominal pain, diarrhea, weight loss, and constipation. It is the leading cause of death worldwide, and this potentially fatal cancer severely afflicts the elderly. Furthermore, early diagnosis is crucial for effective treatment, as it is often more time‐consuming and laborious for experts. This study improved the accuracy of CRC multi‐class classification compared to previous research utilizing diverse datasets, such as NCT‐CRC‐HE‐100 K (100,000 images) and CRC‐VAL‐HE‐7 K (7,180 images). Initially, we utilized various image processing techniques on the NCT‐CRC‐HE‐100 K dataset to improve image quality and noise‐freeness, followed by multiple feature extraction and selection methods to identify prominent features from a large data hub and experimenting with different approaches to select the best classifiers for these critical features. The third ensemble model (XGB‐LightGBM‐RF) achieved an optimum accuracy of 99.63% with 40 prominent features using univariate feature selection methods. Moreover, the third ensemble model also achieved 99.73% accuracy from the CRC‐VAL‐HE‐7 K dataset. After combining two datasets, the third ensemble model achieved 99.27% accuracy. In addition, we trained and tested our model with two different datasets. We used 80% data from NCT‐CRC‐HE‐100 K and 20% data from CRC‐VAL‐HE‐7 K, respectively, for training and testing purposes, while the third ensemble model obtained 98.43% accuracy in multi‐class classification. The results show that this new framework, which was created using the third ensemble model, can help experts figure out what kinds of CRC diseases people are dealing with at the very beginning of an investigation. [ABSTRACT FROM AUTHOR]

Published
2024
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