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Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%; with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words. [ABSTRACT FROM AUTHOR]

Background: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms. Methods: A gender-matched case–control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train–test split (70:30) and tenfold cross-validation approaches. Results: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train–test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation. Conclusion: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services. [ABSTRACT FROM AUTHOR]

Accurate and timely information about production estimates of wheat is useful for policymakers and government planners. The traditional methods for yield forecasting are labor insensitive and time-consuming therefore remote sensing is an effective approach for precise yield forecasting. The study was planned to develop a comprehensive framework for yield forecasting and to assess interannual yield variability in semi-arid regions. For wheat area classification, the peak season Landsat-8 satellite images were acquired, and Top of Atmospheric (TOA) correction was performed. The ground-truthing points of 100 farms were collected from the study area for the training of algorithms. The eight machine learning algorithms were used tune and tested using 10-k fold cross-validation and the best model was used for land cover classification of wheat. For yield forecasting, the temporal normalized difference vegetation index (NDVI) and land surface temperature (LST) were derived for the wheatgrowing season from November to April. A Principal Component Analysis (PCA) was used to variable selection and then Least Absolute Shrinkage Selection Operator (LASSO) analysis was performed to develop coefficients of the yield forecasting model. The developed model was further used in yield forecasting of 10 years (2008-2018) in four semi-arid regions. The predicted yield was compared with Crop Reporting Service (CRS), Pakistan department. The results of all machine learning algorithms showed an accuracy of 88% to 96%, however, the Random forest algorithm showed higher accuracy, which was further used for classification. The wheat estimated area of 6.9% was less than reported by CRS. For interannual variability, the relationship of observed (CRS) and predicted yield of 10 years showed a close relation with R2 ranged from 0.69 to 0.75 in the semi-arid region of Punjab, Pakistan. It was concluded that machine learning algorithms can be used as novel tools for yield forecasting and assessment of interannual yield variability. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is considered as an essential component in hydrological and agro meteorological processes. Its accurate estimation becomes an imperative in the planning and management of irrigation practices. ETo estimation also plays a vital role in improving the irrigation efficiency, water reuse and irrigation scheduling. The conventional physical model of Penmen Montieth (PM56) developed by Food and agriculture organization (FAO) has been recommended worldwide for ETo estimation. This model was firstly used in this study to determine ETo by using required meteorological data and obtained results used as referenced values. Afterward, five data machine learning algorithms/data driven models, support vector machine (SVM), multilayer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN) and cascade correlation neural network (CCNN), were applied to estimate ETo values. The climatic data of maximum and minimum temperatures, wind speed, average relative humidity and sunshine hours of six stations from Pakistan was used to train and test data driven model. Data driven models were also applied on other climatic stations without training data which lie in China, New Zealand and USA to further validate and investigate their performance. Comparison results indicated that model efficiency (ME) and correlation coefficient (r) of SVM were obtained (ranges: ME = 95–99%; r = 0.96–1) maximum for all the selected stations. Alternatively, model errors (RMSE = 0.016, MSE = 0.0001 & MAE = 0.08) for SVM were found minimum in comparison to GMDH, MLP, CCNN and GRNN. In addition, all data driven models show enough divergence from hyper arid to high humid climate except SVM which shows almost identical results for all the climatic zones in comparison to standard FAO-PM56 method. Finally, it can be concluded that SVM could be considered as a reliable alternative method for ETo estimation among data driven models. [ABSTRACT FROM AUTHOR]

Multi-Model Ensembles (MMEs) are often employed to reduce the uncertainties related to GCM simulations/projections. The objective of this study was to evaluate the performance of MMEs developed using machine learning (ML) algorithms with different combinations of GCMs ranked based on their performance and determine the optimum number of GCMs to be included in an MME. In this study ML algorithms; Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Relevance Vector Machine (RVM) were used to develop MMEs for annual, monsoon and winter; precipitation (P), maximum (T max) and minimum (T min) temperature over Pakistan using 36 Coupled Model Intercomparison Project Phase 5 GCMs. GCMs were ranked using Taylor Skill Score for individual seasons and variables, and then using a comprehensive Rating Metric (RM) overall rank of each GCM was determined. It was found that, HadGEM2-AO is the most skilled GCM and IPSL-CM5B-LR is the least skilled GCMs in simulating the 3 climate variables. The performance of MMEs did not improve after the inclusion of about 18 top-ranked GCMs. Thus, it was understood that the optimum performance of MMEs is achieved when about 50% of the top-ranked GCMs are used. The inter-comparison of MMEs developed with ANN, KNN, SVM and RVM revealed that KNN and RVM-based MMEs show better skills. It was found that RVM yields MMEs which show smaller variations in performance over space unlike ANN which displayed large fluctuations in performance over space. KNN and RVM are recommended over SVM and ANN for the development of MMEs over Pakistan. • Optimum performance of multi-model ensemble is achieved with 50% of top-ranked GCMs. • K -Nearest Neighbour and Relevance Vector Machine are good for multi-model ensembles. • Artificial Neural Network multi-model ensembles showed large performance fluctuations in space. • Machine learning-based multi-model ensembles outperformed simple ensmeble mean. [ABSTRACT FROM AUTHOR]