From the socio-critical perspective of mathematical modeling, reflexive discussions about the nature, criteria, and consequences of mathematical models are not a natural consequence of modeling in school. This report is part of a larger study focused on stimulating reflexive discussions in practice employing constructivist grounded theory as a research method. Twenty-seven college algebra students engaged in a 3-week modeling project at a community college in the USA. Audio-recorded group discussions and written reflections were collected to determine how reflexive discussions were taking place. Analysis of students’ actions and reflexive discussions during the modeling project produced four concepts: voicing mathematics, personalizing mathematics, challenging mathematics, and negotiating mathematics. These concepts are integrated into an overall process for stimulating reflexive discussions and are conceptualized as unboxing mathematics. The overarching concept of unboxing mathematics represents one interpretation of how reflexive discussions may be constituted during modeling activities and identifies classroom mathematical practices specific to the socio-critical modeling context of this study.
Community forestry around the world has demonstrated its potential for implementing the Reducing Emissions from Deforestation and Degradation (REDD+) climate change program. Secure tenure rights and access to rule-making are known as contributing to successful community forestry outcomes. Still, the effects of different aspects of rural ‘community agency’ are not well established. We investigate forest governance and conflicts and the relationships between aspects of rural community agency under the REDD+ climate change program in two forest communities—the villages of Fabe and Mosongiseli—near the southern portion of the Korup National Park in Cameroon. Using data from a survey instrument and interviews, we analyze, using “agency theory”, the concept of rural community agency according to dimensions of attitudes, understandings, and empowerment in the two communities in relation to forest governance and conflicts under REDD+. Our findings indicate a variety of power relations (e.g., on the communities’ use and management rights of their lands) and existential threats of conflicts within the communities (e.g., violation of the communities’ free, prior, and informed consent). The results also show that both communities share many of the patterns of diversity and integration to a similar extent. Although there is no definitive distinction between the two communities, the findings suggest that some differences exist in their degree of integration. Understanding and describing the nature of the power relations and threats of conflicts comprises an important component to begin an appreciation for the communities’ user group characteristics as these relate to the REDD+ program when implemented. The implication of this study is that threats of conflicts may increase when the villagers’ perception of the potential costs of losing their lands to REDD+ is formed by their experiences with current restrictions on the use and management rights of their lands.
Labor safety at workplaces is a critical human rights concern in all industries around the world. Coal mines are considered one of the most dangerous workplaces and every year thousands of miners around the world die or get severe injuries in mining accidents. To make efficient technology-based accident mitigation plans for such work environments, the analysis of reasons which cause these accidents is of great value. This study contributes to the coal mines domain and proposed an approach using machine learning techniques to identify the reasons for the accidents. In our approach, a dataset containing the causes for accidents in text form that occurred in the past in coal mines has been used. We performed preprocessing to clean text data and then extract features to train the machine learning model using the term frequency-inverse document frequency (TF-IDF) technique. This study proposed the voting-based hybrid classifier (VHC) which is a combination of three individual machine learning models random forest, support vector classifier, and logistic regression using soft voting criteria. Evaluation of the model has been done in terms of accuracy, precision, recall, and f1 score. VHC outperforms all other stat of the art models by achieving the highest 0.96 accuracy score.
The periodic inspection of railroad tracks is very important to find structural and geometrical problems that lead to railway accidents. Currently, in Pakistan, rail tracks are inspected by an acoustic-based manual system that requires a railway engineer as a domain expert to differentiate between different rail tracks’ faults, which is cumbersome, laborious, and error-prone. This study proposes the use of traditional acoustic-based systems with deep learning models to increase performance and reduce train accidents. Two convolutional neural networks (CNN) models, convolutional 1D and convolutional 2D, and one recurrent neural network (RNN) model, a long short-term memory (LSTM) model, are used in this regard. Initially, three types of faults are considered, including superelevation, wheel burnt, and normal tracks. Contrary to traditional acoustic-based systems where the spectrogram dataset is generated before the model training, the proposed approach uses on-the-fly feature extraction by generating spectrograms as a deep learning model’s layer. Different lengths of audio samples are used to analyze their performance with each model. Each audio sample of 17 s is split into 3 variations of 1.7, 3.4, and 8.5 s, and all 3 deep learning models are trained and tested against each split time. Various combinations of audio data augmentation are analyzed extensively to investigate models’ performance. The results suggest that the LSTM with 8.5 split time gives the best results with the accuracy of 99.7%, the precision of 99.5%, recall of 99.5%, and F1 score of 99.5%
COVID-19 pandemic has caused a global health crisis, resulting in endless efforts to reduce infections, fatalities, and therapies to mitigate its after-effects. Currently, large and fast-paced vaccination campaigns are in the process to reduce COVID-19 infection and fatality risks. Despite recommendations from governments and medical experts, people show conceptions and perceptions regarding vaccination risks and share their views on social media platforms. Such opinions can be analyzed to determine social trends and devise policies to increase vaccination acceptance. In this regard, this study proposes a methodology for analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset. The study relies on two techniques to analyze the sentiments: natural language processing and machine learning. To evaluate the performance of the different lexicon-based methods, different machine and deep learning models are studied. In addition, for sentiment classification, the proposed ensemble model named long short-term memory-gated recurrent neural network (LSTM-GRNN) is a combination of LSTM, gated recurrent unit, and recurrent neural networks. Results suggest that the TextBlob shows better results as compared to VADER and AFINN. The proposed LSTM-GRNN shows superior performance with a 95% accuracy and outperforms both machine and deep learning models. Performance analysis with state-of-the-art models proves the significance of the LSTM-GRNN for sentiment analysis.
Blood cancer has been a growing concern during the last decade and requires early diagnosis to start proper treatment. The diagnosis process is costly and time-consuming involving medical experts and several tests. Thus, an automatic diagnosis system for its accurate prediction is of significant importance. Diagnosis of blood cancer using leukemia microarray gene data and machine learning approach has become an important medical research today. Despite research efforts, desired accuracy and efficiency necessitate further enhancements. This study proposes an approach for blood cancer disease prediction using the supervised machine learning approach. For the current study, the leukemia microarray gene dataset containing 22,283 genes, is used. ADASYN resampling and Chi-squared (Chi2) features selection techniques are used to resolve imbalanced and high-dimensional dataset problems. ADASYN generates artificial data to make the dataset balanced for each target class, and Chi2 selects the best features out of 22,283 to train learning models. For classification, a hybrid logistics vector trees classifier (LVTrees) is proposed which utilizes logistic regression, support vector classifier, and extra tree classifier. Besides extensive experiments on the datasets, performance comparison with the state-of-the-art methods has been made for determining the significance of the proposed approach. LVTrees outperform all other models with ADASYN and Chi2 techniques with a significant 100% accuracy. Further, a statistical significance T -test is also performed to show the efficacy of the proposed approach. Results using k-fold cross-validation prove the supremacy of the proposed model.
With social media’s dominating role in the socio-political landscape, several existing and new forms of racism took place on social media. Racism has emerged on social media in different forms, both hidden and open, hidden with the use of memes and open as the racist remarks using fake identities to incite hatred, violence, and social instability. Although often associated with ethnicity, racism is now thriving based on color, origin, language, cultures, and most importantly religion. Social media opinions and remarks provocating racial differences have been regarded as a serious threat to social, political, and cultural stability and have threatened the peace of different countries. Consequently, social media being the leading source of racist opinions dissemination should be monitored and racism remarks should be detected and blocked timely. This study aims at detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent-Neural Networks (GCR-NN). GRU is on the top in the GCR-NN model to extract the suitable and prominent features from raw text, CNN extracts important features for RNN to make accurate predictions. Obviously, several experiments are conducted to investigate and analyze the performance of the proposed GCR-NN within the scope of machine learning and deep learning models indicating the superior performance of GCR-NN with increased 0.98 accuracy. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments.
Microblogging websites and social media platforms serve as a potential source for mining public opinions and sentiments on a variety of subjects including the prevailing situations in war-afflicted countries. In particular, Twitter has a large number of geotagged tweets that make the analysis of sentiments across time and space possible. This study performs volume analysis and sentiment analysis using LDA (Latent Dirichlet Allocation) and text mining over two datasets collected for different periods. To increase the adequacy and efficacy of the sentiment analysis, a hybrid feature engineering approach is proposed that elevates the performance of machine learning models. Geotagged tweets are used for volume analysis indicating that the highest number of tweets is originated from India, the US, the UK, Pakistan, and Afghanistan. Analysis of positive and negative tweets reveals that negative tweets are mostly originated from India and the US. On the contrary, positive tweets belong to Pakistan and Afghanistan. LDA is used for topic modeling on two datasets containing tweets about the current situation after the Taliban take control of Afghanistan. Topics extracted through LDA suggest that majority of the Afghanistan people seem satisfied with the Taliban’s takeover while the topics from negative tweets reveal that issues discussed in negative tweets are related to the US concerns in Afghanistan. Sentiment analysis over two different datasets indicates that the trend of the sentiments has been shifted positively over three weeks.
The cryptocurrency market has been developed at an unprecedented speed over the past few years. Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Although cryptocurrency ensures legitimate and unique transactions by utilizing cryptographic methods, this industry is still in its inception and serious concerns have been raised about its use. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples’ perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the cryptocurrency which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications including long short term memory (LSTM) and gated recurrent unit (GRU). LSTM and GRU are stacked where the GRU is trained on the features extracted by LSTM. Utilizing term frequency-inverse document frequency, word2vec, and bag of words (BoW) features, several machine learning and deep learning approaches and a proposed ensemble model are investigated. Furthermore, TextBlob and Text2Emotion are studied for emotion analysis with the selected models. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. Results suggest that the performance of machine learning models is comparatively better when BoW features are used. The proposed LSTM-GRU ensemble shows an accuracy of 0.99 for sentiment analysis, and 0.92 for emotion prediction and outperforms both machine learning and state-of-the-art models.
Big data analytics has shown tremendous success in several fields such as businesses, agriculture, health, and meteorology, and education is no exception. Concerning its role in education, it is used to boost students’ learning process by predicting their performance in advance and adapting the relevant instructional design strategies. This study primarily intends to develop a system that can predict students’ performance and help teachers to timely introduce corrective interventions to uplift the performance of low-performing students. As a secondary part of this research, it also explores the potential of collaborative learning as an intervention to act in combination with the prediction system to improve the performance of students. To support such changes, a visualization system is also developed to track and monitor the performance of students, groups, and overall class to help teachers in the regrouping of students concerning their performance. Several well-known machine learning models are applied to predict students performance. Results suggest that experimental groups performed better after treatment than before treatment. The students who took part in each class activity, prepared and submitted their tasks perform much better than other students. Overall, the study found that collaborative learning methods play a significant role to enhance the learning capability of the students.
Citation: Siddiqui, H.U.R.; Shahzad, H.f.; Saleem, A.A.; Khan Khakwani, A.B.; Rustam, F.; Lee, E.; Ashraf, I.; Dudley, S. Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning. Sensors 2021, 21, 8336.
Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction.
Strengthening financial cooperation between BRICS-Eurasian member-countries is the best solution to mitigate trade wars, unfair competition and worsening of international economic relations. In this paper, we analyze and determine cooperation between BRICS and Eurasian union (EAEU) credit markets, taking into consideration their reasonable interaction. The aim of the article is to introduce recommendations aimed at optimizing financial cooperation of developing countries as members of the intergovernmental alliances. The existing data is processed using combination of Bloomberg and Excel tools. Testing of initial hypothesis proved interdependence of credit markets. It is mostly developed and presented in data. The analysis of the research concludes the main industries financed by governments or institutional investors vary depending on the strategic orientation of each country's economy. For example, for the Russian Federation, South Africa, India, Brazil, these sectors are Materials, energy, innovations with domination of the Materials sector as an essential for building mutual value-chains ecosystems. In China, the key industry is the financial sector, which indicates the predominance of the service sector. As for the EAEU countries, most of the loans are directed to state needs, covering external or internal debt (Belarus, Armenia, etc.) and are forced to resort to borrowed funds to cover their needs. Kazakhstan and Russia use credit facility for developing national economies in the interest of the population. Anyway, the research founds out, that the indexes of the emerging credit markets still rely on the production of raw materials and depends to the certain extent on the price movements of commodities. We consider reasonable to take into consideration the international experience in risk management as well as approach conveying better access to loans for commercial entities; improving inter government grants to new industries and taxing relief for manufactured exported goods. In order to break the economic deadlock of some BRICS - EAEU credit markets it is worth to lead harmonized financial policies that would pave the way for mutual development. #COMESYSO1120
The spread of altered media in the form of fake videos, audios, and images, has been largely increased over the past few years. Advanced digital manipulation tools and techniques make it easier to generate fake content and post it on social media. In addition, tweets with deep fake content make their way to social platforms. The polarity of such tweets is significant to determine the sentiment of people about deep fakes. This paper presents a deep learning model to predict the polarity of deep fake tweets. For this purpose, a stacked bi-directional long short-term memory (SBi-LSTM) network is proposed to classify the sentiment of deep fake tweets. Several well-known machine learning classifiers are investigated as well such as support vector machine, logistic regression, Gaussian Naive Bayes, extra tree classifier, and AdaBoost classifier. These classifiers are utilized with term frequency-inverse document frequency and a bag of words feature extraction approaches. Besides, the performance of deep learning models is analyzed including long short-term memory network, gated recurrent unit, bi-direction LSTM, and convolutional neural network+LSTM. Experimental results indicate that the proposed SBi-LSTM outperforms both machine and deep learning models and achieves an accuracy of 0.92.
The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.
Citation: Shafique, R.; Siddiqui, H.-U.-R.; Rustam, F.; Ullah, S.; Siddique, M.A.; Lee, E.; Ashraf, I.; Dudley, S. A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis. Sensors 2021, 21, 6221. https://doi. Abstract: Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and su-perelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.
Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.
Systemic gastrointestinal disorders and oral manifestations are well elucidated in current literature. Many chronic disorders such as cardiovascular disease, stroke, brain health, lung disease, and diabetes are associated with oral manifestations. This bi-directional relationship may be a result of inflammatory response to periodontal pathogens. Inflammatory mediators pronounced in the oral cavity may gain access to the circulation resulting in systemic effects. Proper daily oral care, with prevention as the cornerstone, is the most crucial factor in improving oral health in older adults.
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