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Article: Carbon capture, utilization and storage opportunities to mitigate greenhouse gases.

Rashid, Muhammad Imran / Yaqoob, Zahida / Mujtaba, M A / Kalam, M A / Fayaz, H / Qazi, Atika

2024 Volume 10, Issue 3, Page(s) e25419

Abstract: Carbon capture, utilization and storage (CCUS) technologies are utmost need of the modern era. CCUS technologies adoption is compulsory to keep global warming below 1.5 °C. Mineral carbonation (MC) is considered one of the safest and most viable methods ... ...

Abstract	Carbon capture, utilization and storage (CCUS) technologies are utmost need of the modern era. CCUS technologies adoption is compulsory to keep global warming below 1.5 °C. Mineral carbonation (MC) is considered one of the safest and most viable methods to sequester anthropogenic carbon dioxide (CO
Language	English
Publishing date	2024-02-01
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e25419
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Editorial: Social commerce in the new era.

Yang, Shuiqing / Sun, Yuan / Qazi, Atika / Lin, Jiabao / Haruna, Khalid

Frontiers in psychology

2022 Volume 13, Page(s) 1010357

Language	English
Publishing date	2022-09-13
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2563826-9
ISSN	1664-1078
ISSN	1664-1078
DOI	10.3389/fpsyg.2022.1010357
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Article ; Online: Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets.

Hidayatullah, Ahmad Fathan / Apong, Rosyzie Anna / Lai, Daphne T C / Qazi, Atika

PeerJ. Computer science

2023 Volume 9, Page(s) e1312

Abstract: With the massive use of social media today, mixing between languages in social media text is prevalent. In linguistics, the phenomenon of mixing languages is known as code-mixing. The prevalence of code-mixing exposes various concerns and challenges in ... ...

Abstract	With the massive use of social media today, mixing between languages in social media text is prevalent. In linguistics, the phenomenon of mixing languages is known as code-mixing. The prevalence of code-mixing exposes various concerns and challenges in natural language processing (NLP), including language identification (LID) tasks. This study presents a word-level language identification model for code-mixed Indonesian, Javanese, and English tweets. First, we introduce a code-mixed corpus for Indonesian-Javanese-English language identification (IJELID). To ensure reliable dataset annotation, we provide full details of the data collection and annotation standards construction procedures. Some challenges encountered during corpus creation are also discussed in this paper. Then, we investigate several strategies for developing code-mixed language identification models, such as fine-tuning BERT, BLSTM-based, and CRF. Our results show that fine-tuned IndoBERTweet models can identify languages better than the other techniques. This is the result of BERT's ability to understand each word's context from the given text sequence. Finally, we show that sub-word language representation in BERT models can provide a reliable model for identifying languages in code-mixed texts.
Language	English
Publishing date	2023-06-22
Publishing country	United States
Document type	Journal Article
ISSN	2376-5992
ISSN (online)	2376-5992
DOI	10.7717/peerj-cs.1312
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Migraine headache (MH) classification using machine learning methods with data augmentation.

Khan, Lal / Shahreen, Moudasra / Qazi, Atika / Jamil Ahmed Shah, Syed / Hussain, Sabir / Chang, Hsien-Tsung

Scientific reports

2024 Volume 14, Issue 1, Page(s) 5180

Abstract: Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnosis. ... ...

Abstract	Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnosis. Even though headaches are a common condition with poor diagnostic specificity, they have a significant negative influence on the brain, body, and general human function. In this era of deeply intertwined health and technology, machine learning (ML) has emerged as a crucial force in transforming every aspect of healthcare, utilizing advanced facilities ML has shown groundbreaking achievements related to developing classification and automatic predictors. With this, deep learning models, in particular, have proven effective in solving complex problems spanning computer vision and data analytics. Consequently, the integration of ML in healthcare has become vital, especially in developing countries where limited medical resources and lack of awareness prevail, the urgent need to forecast and categorize migraines using artificial intelligence (AI) becomes even more crucial. By training these models on a publicly available dataset, with and without data augmentation. This study focuses on leveraging state-of-the-art ML algorithms, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), to predict and classify various types of migraines. The proposed models with data augmentations were trained to classify seven various types of migraine. The proposed models with data augmentations were trained to classify seven various types of migraine. The revealed results show that DNN, SVM, KNN, DST, and RF achieved an accuracy of 99.66%, 94.60%, 97.10%, 88.20%, and 98.50% respectively with data augmentation highlighting the transformative potential of AI in enhancing migraine diagnosis.
MeSH term(s)	Humans ; Artificial Intelligence ; Machine Learning ; Neural Networks, Computer ; Algorithms ; Migraine Disorders/diagnosis ; Support Vector Machine
Language	English
Publishing date	2024-03-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-55874-0
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Article ; Online: XML‐LightGBMDroid

Khandaker Mohammad Mohi Uddin / Nitish Biswas / Sarreha Tasmin Rikta / Samrat Kumar Dey / Atika Qazi

Engineering Reports, Vol 5, Iss 11, Pp n/a-n/a (2023)

A self‐driven interactive mobile application utilizing explainable machine learning for breast cancer diagnosis

2023

Abstract: Abstract Nowadays, breast cancer detection and diagnosis are done using machine learning algorithms. It can enhance cancer understanding and help in treatment selection and diagnosis. But many reliable decision assistance systems have been developed as “ ... ...

Abstract	Abstract Nowadays, breast cancer detection and diagnosis are done using machine learning algorithms. It can enhance cancer understanding and help in treatment selection and diagnosis. But many reliable decision assistance systems have been developed as “black boxes,” or devices that conceal their internal workings from the user. In fact, this method's output is difficult to understand, which makes it difficult for doctors to use it. This study uses explainable machine learning to investigate a technique for more promptly and accurately predicting breast cancer. The data is obtained from Kaggle to generate a machine learning (ML) model that forecasts the occurrence of breast cancer and Shapley Additive exPlanations (SHAP) are used to interpret the model's forecasts. To forecast the development of this disease, explainable machine learning (XML) model based on gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM) is built. The investigation's findings show that the LightGBM is capable of a maximum accuracy of 99%. An explainable ML has been demonstrated here which may produce an explicit understanding of how models generate their predictions, which is critical in boosting the confidence and acceptance of cutting‐edge ML methods in oncology and healthcare in general. Finally, a mobile app is also developed, integrating the best model.
Keywords	black box ; breast cancer ; ensemble models ; explainable machine learning ; LightGBM ; mobile app ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006 ; 670
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: SentiTAM: Sentiments centered integrated framework for mobile learning adaptability in higher education

Qazi, Atika / Hasan, Najmul / Owusu-Ansah, Christopher M. / Hardaker, Glenn / Dey, Samrat Kumar / Haruna, Khalid

Heliyon. 2023 Jan., v. 9, no. 1 p.e12705-

2023

Abstract: Online communities provide facilities to share public opinions and or sentiments on a wide range of subjects, from routine topics to vital issues of critical interest. Nowadays, many higher education institutions (HEIs) recognize the value of students' ... ...

Abstract	Online communities provide facilities to share public opinions and or sentiments on a wide range of subjects, from routine topics to vital issues of critical interest. Nowadays, many higher education institutions (HEIs) recognize the value of students' sentiments and evaluate users' concerns for the successful adaptation of mobile learning applications (MLAs). While digital learning has been extensively studied previously, little has been known about why MLA is underutilized. Therefore, this study extends the literature by proposing the SentiTAM model underlying technology acceptance model (TAM), and students' sentiments on MLA platforms. A self-administered cross-sectional survey of 350 MLA users' data was analyzed through structural equation modeling (SEM) using the AMOS package program. In addition, we have performed sentiment analysis on students' opinions gathered through Google discussion forums and Twitter. The results show that MLA use intention is strongly influenced by sentiments and self-motivation, while perceived usefulness and perceived ease of use directly influence MLA usage. To the best of our knowledge, this study is the first attempt in MLA that investigates several vital factors, including sentiments as a multi-perspective tool and motivational factors with core constructs of TAM. The findings assist developing countries make smart decisions about how to use MLA with emerging technology.
Keywords	cross-sectional studies ; education ; equations ; models ; Sentiment analysis ; Technology acceptance model (TAM) ; SenitTAM ; Higher education ; Mobile learning applications (MLAs)
Language	English
Dates of publication	2023-01
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2022.e12705
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Global landscape of COVID-19 vaccination progress: insight from an exploratory data analysis.

Dey, Samrat Kumar / Rahman, Md Mahbubur / Siddiqi, Umme Raihan / Howlader, Arpita / Tushar, Md Arifuzzaman / Qazi, Atika

Human vaccines & immunotherapeutics

2022 Volume 18, Issue 1, Page(s) 2025009

Abstract: The next big step in combating the COVID-19 pandemic will be gaining widespread acceptance of a vaccination campaign for SARS-CoV-2. This study aims to report detailed Spatiotemporal analysis and result-oriented storytelling of the COVID-19 vaccination ... ...

Abstract	The next big step in combating the COVID-19 pandemic will be gaining widespread acceptance of a vaccination campaign for SARS-CoV-2. This study aims to report detailed Spatiotemporal analysis and result-oriented storytelling of the COVID-19 vaccination campaign across the globe. An exploratory data analysis (EDA) with interactive data visualization using various python libraries was conducted. The results show that, globally, with the rapid vaccine development and distribution, people from the different regions are also getting vaccinated and revealing their positive intent toward the COVID-19 vaccination. The outcomes of this exploration also established that mass vaccination campaigns in populated countries including Brazil, China, India, and the US reduced the number of daily COVID-19 deaths and confirmed cases. Overall, our findings contribute to current policy-relevant research by establishing a link between increasing immunization rates and lowering COVID-19's rising curve.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19 Vaccines ; Data Analysis ; Humans ; Pandemics/prevention & control ; SARS-CoV-2 ; Vaccination
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2022-01-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2664176-8
ISSN	2164-554X ; 2164-5515
ISSN (online)	2164-554X
ISSN	2164-5515
DOI	10.1080/21645515.2021.2025009
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Article: Evaluating sustainable municipal solid waste management scenarios: A multicriteria decision making approach.

Mujtaba, M A / Munir, Adeel / Imran, Shahid / Nasir, Muhammad Kamran / Muhayyuddin, M Ghulam / Javed, Abdullah / Mehmood, Amjad / Habila, Mohamed A / Fayaz, H / Qazi, Atika

Heliyon

2024 Volume 10, Issue 4, Page(s) e25788

Abstract: Due to increasing urbanization and population growth, municipal solid waste management (MSWM) is a significant environmental concern in developing countries. Inadequate waste management systems lead to environmental pollution, health hazards, and ... ...

Abstract	Due to increasing urbanization and population growth, municipal solid waste management (MSWM) is a significant environmental concern in developing countries. Inadequate waste management systems lead to environmental pollution, health hazards, and economic losses. While considering the challenges and limitations, policymakers and authorities need to opt for such waste management scenarios that are environmentally friendly and resolve energy issues. Ten MSWM scenarios were developed and evaluated using seven different criteria. Four multi-criteria decision-making (MCDM) techniques, namely fuzzy logic, AHP, TOPSIS, and PROMETHEE II, were employed to rank the scenarios and identify the most appropriate option for solid waste management in Lahore. This study highlights that the optimal waste management approach comprises a composition of 54% anaerobic digestion, 37% gasification, and 9% landfill technologies. These percentages collectively represent the most suitable and effective strategies for the city's waste management needs. All the MCDM techniques consistently produce similar results. These scenarios have broader applicability across cities in Central Asia and beyond. The study's findings are aligned to promote sustainable and environmentally friendly MSWM practices. These findings endorse implementing strategies and measures aimed at fostering environmental sustainability and the responsible handling of waste, serving as a valuable reference for various regions.
Language	English
Publishing date	2024-02-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2024.e25788
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Article: SentiTAM: Sentiments centered integrated framework for mobile learning adaptability in higher education.

Qazi, Atika / Hasan, Najmul / Owusu-Ansah, Christopher M / Hardaker, Glenn / Dey, Samrat Kumar / Haruna, Khalid

Heliyon

2022 Volume 9, Issue 1, Page(s) e12705

Abstract	Online communities provide facilities to share public opinions and or sentiments on a wide range of subjects, from routine topics to vital issues of critical interest. Nowadays, many higher education institutions (HEIs) recognize the value of students' sentiments and evaluate users' concerns for the successful adaptation of mobile learning applications (MLAs). While digital learning has been extensively studied previously, little has been known about why MLA is underutilized. Therefore, this study extends the literature by proposing the SentiTAM model underlying technology acceptance model (TAM), and students' sentiments on MLA platforms. A self-administered cross-sectional survey of 350 MLA users' data was analyzed through structural equation modeling (SEM) using the AMOS package program. In addition, we have performed sentiment analysis on students' opinions gathered through Google discussion forums and Twitter. The results show that MLA use intention is strongly influenced by sentiments and self-motivation, while perceived usefulness and perceived ease of use directly influence MLA usage. To the best of our knowledge, this study is the first attempt in MLA that investigates several vital factors, including sentiments as a multi-perspective tool and motivational factors with core constructs of TAM. The findings assist developing countries make smart decisions about how to use MLA with emerging technology.
Language	English
Publishing date	2022-12-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2022.e12705
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SentiTAM

Atika Qazi / Najmul Hasan / Christopher M. Owusu-Ansah / Glenn Hardaker / Samrat Kumar Dey / Khalid Haruna

Heliyon, Vol 9, Iss 1, Pp e12705- (2023)

Sentiments centered integrated framework for mobile learning adaptability in higher education

2023

Abstract	Online communities provide facilities to share public opinions and or sentiments on a wide range of subjects, from routine topics to vital issues of critical interest. Nowadays, many higher education institutions (HEIs) recognize the value of students' sentiments and evaluate users' concerns for the successful adaptation of mobile learning applications (MLAs). While digital learning has been extensively studied previously, little has been known about why MLA is underutilized. Therefore, this study extends the literature by proposing the SentiTAM model underlying technology acceptance model (TAM), and students' sentiments on MLA platforms. A self-administered cross-sectional survey of 350 MLA users' data was analyzed through structural equation modeling (SEM) using the AMOS package program. In addition, we have performed sentiment analysis on students' opinions gathered through Google discussion forums and Twitter. The results show that MLA use intention is strongly influenced by sentiments and self-motivation, while perceived usefulness and perceived ease of use directly influence MLA usage. To the best of our knowledge, this study is the first attempt in MLA that investigates several vital factors, including sentiments as a multi-perspective tool and motivational factors with core constructs of TAM. The findings assist developing countries make smart decisions about how to use MLA with emerging technology.
Keywords	Sentiment analysis ; Technology acceptance model (TAM) ; SenitTAM ; Higher education ; Mobile learning applications (MLAs) ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	020
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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