Migraine headache (MH) classification using machine learning methods with data augmentation.

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.

Evaluating sustainable municipal solid waste management scenarios: A multicriteria decision making approach.

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.

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

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 (CO2). MC is an exothermic reaction and occur naturally in the subsurface because of fluid-rock interactions with serpentinite. In serpentine carbonation, CO2 reacts with magnesium to produce carbonates. This article covers CO2 mitigation technologies especially mineral carbonation, mineral carbonation by natural and industrial materials, mineral carbonation feedstock availability in Pakistan, detailed characterization of serpentine from Skardu serpentinite belt, geo sequestration, oceanic sequestration, CO2 to urea and CO2 to methanol and other chemicals. Advantages, disadvantages, and suitability of these technologies is discussed. These technologies are utmost necessary for Pakistan as recent climate change induced flooding devastated one third of Pakistan affecting millions of families. Hence, Pakistan must store CO2 through various CCUS technologies.

Corpus creation and language identification for code-mixed Indonesian-Javanese-English Tweets.

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.

SentiTAM: Sentiments centered integrated framework for mobile learning adaptability in higher education.

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.

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

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.

Gender differences in information and communication technology use & skills: a systematic review and meta-analysis.

Even though information and communication technology (ICT) is essential for everyday life and has gained considerable attention in education and other sectors, it also carries individual differences in its use and relevant skills. This systematic review aims to examine the gender differences in ICT use and skills for learning through technology. A comprehensive search of eight journal databases and a specific selection criterion was carried out to exclude articles that match our stated exclusion criteria. We included 42 peer-reviewed empirical publications and conference proceedings published between 2006 and 2020. For a subsample of studies, we performed a small-scale meta-analysis to quantify possible gender differences in ICT use and skills. A random-effects model uncovered a small and positive, yet not significant, effect size in favor of boys (g = 0.17, 95% CI [-0.01, 0.36]). However, this finding needs to be further backed by large-scale meta-analyses, including more study samples and a broader set of ICT use and skills measures. We highlight several concerns that should be addressed and more thoroughly in collaboration with one another to better IT skills and inspire new policies to increase the quality of ICT use. The findings from this review further suggest implications and present existing research challenges and point to future research directions.

Adaption of distance learning to continue the academic year amid COVID-19 lockdown.

This work investigates the use of distance learning in saving students' academic year amid COVID-19 lockdown. It assesses the adoption of distance learning using various online application tools that have gained widespread attention during the coronavirus infectious disease 2019 (COVID-19) pandemic. Distance learning thrives as a legitimate alternative to classroom instructions, as major cities around the globe are locked down amid the COVID-19 pandemic. To save the academic year, educational institutions have reacted to the situation impulsively and adopted distance learning platforms using online resources. This study surveyed random undergraduate students to identify the impact of trust in formal and informal information sources, awareness and the readiness to adopt distance learning. In this study, we have hypothesized that adopting distance learning is an outcome of situational awareness and readiness, which is achieved by the trust in the information sources related to distance learning. The findings indicate that trust in information sources such as institute and media information or interpersonal communication related to distance learning programs is correlated with awareness (ß = 0.423, t = 12.296, p = 0.000) and contribute to readiness (ß = 0.593, t = 28.762, p = 0.001). The structural model path coefficient indicates that readiness strongly influences the adoption of distance learning (ß = 0.660, t = 12.798, p = 0.000) amid the COVID-19 pandemic. Our proposed model recorded a predictive relevance (Q2) of 0.377 for awareness, 0.559 for readiness, and 0.309 for the adoption of distance learning, which explains how well the model and its parameter estimates reconstruct the values. This study concludes with implications for further research in this area.

Conventional to online education during COVID-19 pandemic: Do develop and underdeveloped nations cope alike.

BACKGROUND: Educational institutes around the globe are facing challenges of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Online learning is being carried out to avoid face to face contact in emergency scenarios such as coronavirus infectious disease 2019 (COVID-19) pandemic. Students need to adapt to new roles of learning through information technology to succeed in academics amid COVID-19. OBJECTIVE: However, access and use of online learning resources and its link with satisfaction of students amid COVID-19 are critical to explore. Therefore, in this paper, we aimed to assess and compare the access & use of online learning of Bruneians and Pakistanis amid enforced lockdown using a five-items satisfaction scale underlying existing literature. METHOD: For this, a cross-sectional study was done in the first half of June 2020 after the pandemic situation among 320 students' across Pakistan and Brunei with a pre-defined questionnaire. Data were analyzed with statistical software package for social sciences (SPSS) 2.0. RESULTS: The finding showed that there is a relationship between students' satisfaction and access & use of online learning. Outcomes of the survey suggest that Bruneian are more satisfied (50%) with the use of online learning amid lockdown as compared to Pakistanis (35.9%). Living in the Urban area as compared to a rural area is also a major factor contributing to satisfaction with the access and use of online learning for both Bruneian and Pakistanis. Moreover, previous experience with the use of online learning is observed prevalent among Bruneians (P = .000), while among friends and family is using online learning (P = .000) were encouraging factors contributed to satisfaction with the use of online learning among Pakistanis amid COVID-19. Correlation results suggest that access and use factors of online learning amid COVID-19 were positively associated with satisfaction among both populations amid COVID-19 pandemic. However, Bruneian is more satisfied with internet access (r = 0.437, P < .000) and affordability of gadgets (r = 0.577, P < .000) as compare to Pakistanis (r = 0.176, P < .050) and (r = 0.152, P < .050). CONCLUSION: The study suggested that it is crucial for the government and other policymakers worldwide to address access and use of online learning resources of their populace amid pandemic.

Analyzing situational awareness through public opinion to predict adoption of social distancing amid pandemic COVID-19.

COVID-19 pandemic has affected over 100 countries in a matter of weeks. People's response toward social distancing in the emerging pandemic is uncertain. In this study, we evaluated the influence of information (formal and informal) sources on situational awareness of the public for adopting health-protective behaviors such as social distancing. For this purpose, a questionnaire-based survey was conducted. The hypothesis proposed suggests that adoption of social distancing practices is an outcome of situational awareness which is achieved by the information sources. Results suggest that information sources, formal (P = .001) and informal (P = 0.007) were found to be significantly related to perceived understanding. Findings also indicate that social distancing is significantly influenced by situational awareness, P = .000. It can, therefore, be concluded that an increase in situational awareness in times of public health crisis using formal information sources can significantly increase the adoption of protective health behavior and in turn contain the spread of infectious diseases.

Enhancing business intelligence by means of suggestive reviews.

Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.

A preliminary investigation of user perception and behavioral intention for different review types: customers and designers perspective.

Existing opinion mining studies have focused on and explored only two types of reviews, that is, regular and comparative. There is a visible gap in determining the useful review types from customers and designers perspective. Based on Technology Acceptance Model (TAM) and statistical measures we examine users' perception about different review types and its effects in terms of behavioral intention towards using online review system. By using sample of users (N = 400) and designers (N = 106), current research work studies three review types, A (regular), B (comparative), and C (suggestive), which are related to perceived usefulness, perceived ease of use, and behavioral intention. The study reveals that positive perception of the use of suggestive reviews improves users' decision making in business intelligence. The results also depict that type C (suggestive reviews) could be considered a new useful review type in addition to other types, A and B.