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Book ; Online: Textual Toxicity in Social Media

Rashid, Mohammad Mamun Or

Understanding the Bangla Toxic Language Expressed in Facebook Comment

2023

Abstract: Social Media is a repository of digital literature including user-generated content. The users of social media are expressing their opinion with diverse mediums such as text, emojis, memes, and also through other visual and textual mediums. A major ... ...

Abstract	Social Media is a repository of digital literature including user-generated content. The users of social media are expressing their opinion with diverse mediums such as text, emojis, memes, and also through other visual and textual mediums. A major portion of these media elements could be treated as harmful to others and they are known by many words including Cyberbullying and Toxic Language . The goal of this research paper is to analyze a curated and value-added dataset of toxic language titled ToxLex_bn . It is an exhaustive wordlist that can be used as classifier material to detect toxicity in social media. The toxic language/script used by the Bengali community as cyberbullying, hate speech and moral policing became major trends in social media culture in Bangladesh and West Bengal. The toxicity became so high that the victims has to post as a counter or release explanation video for the haters. Most cases are pointed to women celebrity and their relation, dress, lifestyle are became trolled and toxicity flooded in comments boxes. Not only celebrity bashing but also hates occurred between Hindu Muslims, India-Bangladesh, Two opponents of 1971 and these are very common for virtual conflict in the comment thread. Even many times facebook comment causes sue and legal matters in Bangladesh and thus it requires more study. In this study, a Bangla toxic language dataset has been analyzed which was inputted by the user in Bengali script & language. For this, about 1968 unique bigrams or phrases as wordlists have been analyzed which are derived from 2207590 comments. It is assumed that this analysis will reinforce the detection of Bangla's toxic language used in social media and thus cure this virtual disease.
Keywords	Computer Science - Computation and Language
Subject code	410
Publishing date	2023-12-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Can digital twin efforts shape microorganism-based alternative food?

Helmy, Mohamed / Elhalis, Hosam / Rashid, Md Mamunur / Selvarajoo, Kumar

Current opinion in biotechnology

2024 Volume 87, Page(s) 103115

Abstract: With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing ...

Abstract	With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.
Language	English
Publishing date	2024-03-27
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1052045-4
ISSN	1879-0429 ; 0958-1669
ISSN (online)	1879-0429
ISSN	0958-1669
DOI	10.1016/j.copbio.2024.103115
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: ToxLex_bn: A Curated Dataset of Bangla Toxic Language Derived from Facebook Comment

Rashid, Mohammad Mamun Or

Data in Brief. 2022 June 20,

2022

Abstract: Toxic Language in social media is a newly emerging virtual disorder of human society. Detecting toxic language is an NLP task that requires a Dataset of utterances [1]. For the Bangla language, very few datasets have been developed on toxicity or similar ...

Abstract	Toxic Language in social media is a newly emerging virtual disorder of human society. Detecting toxic language is an NLP task that requires a Dataset of utterances [1]. For the Bangla language, very few datasets have been developed on toxicity or similar concepts [2]. A dataset has been developed using user-generated content from Facebook and that will cover the demographic and thematic distribution of Bangla toxic language generated on the web. Therefore, 2207590 comments have been collected, annotated, and thus extract about 1959 unique bigrams as utterances, which were considered as base-entry of a toxic language dataset. The core derivatives of the dataset are bigram-based wordlists, which are annotated inductively and divided into 08 thematic classes that give some ideas on toxicity variations found in the Bengali community. These thematic classes cover political hate speech [3] and misogynist bullies dominantly. However, these thematic labels will serve as classifiers in the text classification process through machine learning. In addition to the thematic classification labels, this dataset includes some additional features such as imprecise meanings in English, IPA transliteration, real occurrences in the source pages, spelling standards, and degree of toxicity. As this is a dataset of utterance, it has de-identified and anonymous entries and no difficulties for public disclosure. Therefore, we consider this dataset as Toxic lexicon (Toxlex) as an exhaustive wordlist that is essentially a curated value-added and analyzed dataset which can be used as classifier material to detect toxicity in social media.
Keywords	data collection ; humans ; politics ; speech ; toxicity ; value added
Language	English
Dates of publication	2022-0620
Publishing place	Elsevier Inc.
Document type	Article
Note	Pre-press version
ZDB-ID	2786545-9
ISSN	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2022.108416
Database	NAL-Catalogue (AGRICOLA)

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Article: Innovative Strategies for Early Autism Diagnosis: Active Learning and Domain Adaptation Optimization.

Alam, Mohammad Shafiul / Elsheikh, Elfatih A A / Suliman, F M / Rashid, Muhammad Mahbubur / Faizabadi, Ahmed Rimaz

Diagnostics (Basel, Switzerland)

2024 Volume 14, Issue 6

Abstract: The early diagnosis of autism spectrum disorder (ASD) encounters challenges stemming from domain variations in facial image datasets. This study investigates the potential of active learning, particularly uncertainty-based sampling, for domain adaptation ...

Abstract	The early diagnosis of autism spectrum disorder (ASD) encounters challenges stemming from domain variations in facial image datasets. This study investigates the potential of active learning, particularly uncertainty-based sampling, for domain adaptation in early ASD diagnosis. Our focus is on improving model performance across diverse data sources. Utilizing the Kaggle ASD and YTUIA datasets, we meticulously analyze domain variations and assess transfer learning and active learning methodologies. Two state-of-the-art convolutional neural networks, Xception and ResNet50V2, pretrained on distinct datasets, demonstrate noteworthy accuracies of 95% on Kaggle ASD and 96% on YTUIA, respectively. However, combining datasets results in a modest decline in average accuracy, underscoring the necessity for effective domain adaptation techniques. We employ uncertainty-based active learning to address this, which significantly mitigates the accuracy drop. Xception and ResNet50V2 achieve 80% and 79% accuracy when pretrained on Kaggle ASD and applying active learning on YTUIA, respectively. Our findings highlight the efficacy of uncertainty-based active learning for domain adaptation, showcasing its potential to enhance accuracy and reduce annotation needs in early ASD diagnosis. This study contributes to the growing body of literature on ASD diagnosis methodologies. Future research should delve deeper into refining active learning strategies, ultimately paving the way for more robust and efficient ASD detection tools across diverse datasets.
Language	English
Publishing date	2024-03-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics14060629
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Article: Biomarker Discovery for Hepatocellular Carcinoma in Patients with Liver Cirrhosis Using Untargeted Metabolomics and Lipidomics Studies.

Rashid, Md Mamunur / Varghese, Rency S / Ding, Yuansong / Ressom, Habtom W

Metabolites

2023 Volume 13, Issue 10

Abstract: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the ... ...

Abstract	Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.
Language	English
Publishing date	2023-10-02
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo13101047
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Article ; Online: Current understanding of plant-microbe interaction through the lenses of multi-omics approaches and their benefits in sustainable agriculture.

Diwan, Deepti / Rashid, Md Mahtab / Vaishnav, Anukool

Microbiological research

2022 Volume 265, Page(s) 127180

Abstract: The success of sustainable agricultural practices has now become heavily dependent on the interactions between crop plants and their associated microbiome. Continuous advancement in high throughput sequencing platforms, omics-based approaches, and gene ... ...

Abstract	The success of sustainable agricultural practices has now become heavily dependent on the interactions between crop plants and their associated microbiome. Continuous advancement in high throughput sequencing platforms, omics-based approaches, and gene editing technologies has remarkably accelerated this area of research. It has enabled us to characterize the interactions of plants with associated microbial communities more comprehensively and accurately. Furthermore, the genomic and post-genomic era has significantly refined our perspective toward the complex mechanisms involved in those interactions, opening new avenues for efficiently deploying the knowledge in developing sustainable agricultural practices. This review focuses on our fundamental understanding of plant-microbe interactions and the contribution of existing multi-omics approaches, including those under active development and their tremendous success in unraveling different aspects of the complex network between plant hosts and microbes. In addition, we have also discussed the importance of sustainable and eco-friendly agriculture and the associated outstanding challenges ahead.
MeSH term(s)	Agriculture ; Fabaceae ; Microbial Interactions ; Microbiota/genetics ; Plants
Language	English
Publishing date	2022-09-06
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	1189614-0
ISSN	1618-0623 ; 0944-5013
ISSN (online)	1618-0623
ISSN	0944-5013
DOI	10.1016/j.micres.2022.127180
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Article ; Online: Coupling protocol of interlocked feedback oscillators in circadian clocks.

Rashid, Md Mamunur / Kurata, Hiroyuki

Journal of the Royal Society, Interface

2020 Volume 17, Issue 167, Page(s) 20200287

Abstract: Circadian rhythms (approx. 24 h) show the robustness of key oscillatory features such as phase, period and amplitude against external and internal variations. The robustness ... ...

Abstract	Circadian rhythms (approx. 24 h) show the robustness of key oscillatory features such as phase, period and amplitude against external and internal variations. The robustness of
MeSH term(s)	Animals ; Circadian Clocks ; Circadian Rhythm ; Drosophila ; Feedback
Language	English
Publishing date	2020-06-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2020.0287
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Book ; Article ; Online: Berkeley Blues; Ford Community Corps Partnership

Rashid, Muhammad Mustafa

Integrating Environmental Ethic, Bioethics and the Ethics of Emerging Technology into a Comprehensive Leadership Philosophy. A Regional Study, Detroit Michigan. (Covid, Edition)

2019

Abstract: The following project was conducted in partnership with University of Detroit Mercy and Ford Community Corps. Multiple non-profits were approached to make this project possible such as; ERACCE, Detroit Audubon, Detroit International Wild-Life Refuge, ... ...

Abstract	The following project was conducted in partnership with University of Detroit Mercy and Ford Community Corps. Multiple non-profits were approached to make this project possible such as; ERACCE, Detroit Audubon, Detroit International Wild-Life Refuge, Belle Isle. The non-profit ERACCE has provided the criteria of analyzing power in the organizations that are at risk of environmental violations or have had environmental violations. Furthermore, the non-profit has asked for a comparison of power between non-profits working within the sector, such as Detroit Audubon, Detroit International Wild Life Refuge and Detroiters Working for Environmental Justice and the business sector. Hence, this is where ERACCE believes the gap in power to be and hence, environmental injustice and satisfying the proposals need to impact the community. To this end they have provided a sampling of questions that the interview/research should answer. The project has also been written to enhance Michigan’s competitive advantage in; conservation, environmental stewardship, civil rights, industrial innovation, and entrepreneurship as put forth by the Environmental Justice Workgroup in 2018. Furthermore, the rationale that has been provided is the increase in both the private sector and public sector awareness towards sustainability and push towards higher levels of sustainability by CEO’s. Two Nobel Laureates have been awarded a joint Nobel Peace Prize in integrating technological and environmental advances into economic theory. His Holiness Pope Francis wrote an encyclical towards integrating environmental ethics into religious faith followed by an effort with Notre Dame and Oxford to establish a center to focus on matters of ecology. UN Sustainable goals have been established and work has been done to map out the frontier of sustainable technologies. Furthermore, during the course of the year long projection March 19th the state of Michigan entered into a lock-down due to the pandemic termed Covid-19 and the project and scope were modified to reflect this change. The research confirms the hypothesis that there is an imbalance of power between the business community and the community involved in the work of environmental injustice issues.
Keywords	E6 - Macroeconomic Policy ; Macroeconomic Aspects of Public Finance ; and General Outlook ; H7 - State and Local Government ; Intergovernmental Relations ; I0 - General ; J0 - General ; K2 - Regulation and Business Law ; L0 - General ; M10 - General ; M14 - Corporate Culture ; Diversity ; Social Responsibility ; M21 - Business Economics ; N80 - General ; International ; or Comparative ; N82 - U.S. ; Canada: 1913- ; O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights ; Q51 - Valuation of Environmental Effects ; Q57 - Ecological Economics: Ecosystem Services ; Biodiversity Conservation ; Bioeconomics ; Industrial Ecology ; Q58 - Government Policy ; R10 - General ; covid19
Subject code	333 ; 320
Language	English
Publishing date	2019-03-19
Publishing country	de
Document type	Book ; Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Predictability of Extreme Sea Level Variations Along the U.S. Coastline

Rashid, M. M. / Wahl, T.

Journal of geophysical research. 2020 Sept., v. 125, no. 9

2020

Abstract: Extreme sea level variability (excluding the effects of mean sea level (MSL) and long‐period tidal cycles) at decadal to multidecadal time scales is significant along the U.S. coastlines and can modulate coastal flood risk in addition to long‐term MSL ... ...

Abstract	Extreme sea level variability (excluding the effects of mean sea level (MSL) and long‐period tidal cycles) at decadal to multidecadal time scales is significant along the U.S. coastlines and can modulate coastal flood risk in addition to long‐term MSL rise. Therefore, understanding the climatic drivers and ultimately predicting these low‐frequency variations are important. Extreme sea level indicators are used to represent the variations in 100‐year return water levels, estimated with a nonstationary extreme value analysis. Here, we develop prediction models in the frequency domain. Extreme sea level indicators (response) and potential predictors (traditional climate indices, sea level pressure [SLP], or sea surface temperature [SST]) are decomposed into subseries corresponding to predefined frequencies using discrete wavelet transform (DWT), and regression models are formulated for each frequency separately. In the case of traditional climate indices, subseries of climate indices that provide the highest correlation with the corresponding subseries of indicators are used in the regression models, and original indicators are reconstructed by aggregating predicted subseries. Tailored climate indices are developed for each frequency band by averaging wavelet decomposed subseries of SLP or SST from grid locations where correlations with corresponding decomposed subseries of extreme sea level indicators are highest and robust. Models with wavelet filtered climate indices reproduce the variability and general trends of the indicators. The use of tailored indices further improves the model performance in predicting extreme sea level variations. Model performance in terms of Nash‐Sutcliffe efficiency statistics varies from 0.54 to 0.93. Prediction of extreme sea level indicators using tailored indices derived from SLP and SST of initialized decadal climate model simulations is also tested to facilitate progress toward forecasting extreme sea level variations at decadal time scales.
Keywords	climate models ; coasts ; geophysics ; model validation ; prediction ; research ; risk ; sea level ; surface water temperature ; wavelet
Language	English
Dates of publication	2020-09
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	161667-5
ISSN	2169-9291 ; 2169-9275 ; 0148-0227 ; 0196-2256
ISSN (online)	2169-9291
ISSN	2169-9275 ; 0148-0227 ; 0196-2256
DOI	10.1029/2020JC016295
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data

Rashid, Md Mobasshir / Rahman, Rezaur / Hasan, Samiul

A Deep Learning Approach

2023

Abstract: Traffic prediction during hurricane evacuation is essential for optimizing the use of transportation infrastructures. It can reduce evacuation time by providing information on future congestion in advance. However, evacuation traffic prediction can be ... ...

Abstract	Traffic prediction during hurricane evacuation is essential for optimizing the use of transportation infrastructures. It can reduce evacuation time by providing information on future congestion in advance. However, evacuation traffic prediction can be challenging as evacuation traffic patterns is significantly different than regular period traffic. A data-driven traffic prediction model is developed in this study by utilizing traffic detector and Facebook movement data during Hurricane Ian, a rapidly intensifying hurricane. We select 766 traffic detectors from Florida's 4 major interstates to collect traffic features. Additionally, we use Facebook movement data collected during Hurricane Ian's evacuation period. The deep-learning model is first trained on regular period (May-August 2022) data to understand regular traffic patterns and then Hurricane Ian's evacuation period data is used as test data. The model achieves 95% accuracy (RMSE = 356) during regular period, but it underperforms with 55% accuracy (RMSE = 1084) during the evacuation period. Then, a transfer learning approach is adopted where a pretrained model is used with additional evacuation related features to predict evacuation period traffic. After transfer learning, the model achieves 89% accuracy (RMSE = 514). Adding Facebook movement data further reduces model's RMSE value to 393 and increases accuracy to 93%. The proposed model is capable to forecast traffic up to 6-hours in advance. Evacuation traffic management officials can use the developed traffic prediction model to anticipate future traffic congestion in advance and take proactive measures to reduce delays during evacuation.
Keywords	Computer Science - Machine Learning
Subject code	380
Publishing date	2023-11-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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