COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples’ death is not only linked to its infection but also to peoples’ mental states and sentiments triggered by the fear of the virus. People’s sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples’ sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods.

The increasing demand for improving the high-field (16-22 T) performance of Nb3Sn conductors requires a better understanding of the properties of modern wires much closer to irreversibility field, HIrr. In this study we investigated the impact of Ta, Ti and Hf doping on the high-field pinning properties, the upper critical field, Hc2, and HIrr. We found that the pinning force curves of commercial Ti and Ta doped wires at different temperatures do not scale and that the Kramer extrapolation, typically used by magnet designers to estimate highfield critical current density and magnet operational margins from lower field data, is not reliable and significantly overestimates the actual HIrr. In contrast, new laboratory scale conductors made with Nb-Ta-Hf alloy have improved high-field Jc performance and, despite contributions by both grain boundary and point defect pinning mechanisms, have more predictable high-field behavior. Using Extended X-ray Absorption Fine Structure spectroscopy, EXAFS, we found that for the commercial Ta and Ti doped conductors, the Ta site occupancy in the A15 structure gradually changes with the heat treatment temperature whereas Ti is always located on the Nb site with clear consequences for Hc2. This work reveals the still limited understanding of what determines Hc2, HIrr and the high-field Jc performance of Nb3Sn and the complexity of optimizing these conductors so that they can reach their full potential for high-field applications.

An automated text classification is a well-studied problem in text mining which generally demands the automatic assignment of a label or class to a particular text documents on the basis of its content. To design a computer program that learns the model form training data to assign the specific label to unseen text document, many researchers has applied deep learning technologies. For Nepali language, this is first attempt to use deep learning especially Recurrent Neural Network (RNN) and compare its performance to traditional Multilayer Neural Network (MNN). In this study, the Nepali texts were collected from online News portals and their pre-processing and vectorization was done. Finally deep learning classification framework was designed and experimented for ten experiments: five for Recurrent Neural Network and five for Multilayer Neural Network. On comparing the result of the MNN and RNN, it can be concluded that RNN outperformed the MNN as the highest accuracy achieved by MNN is 48 % and highest accuracy achieved by RNN is 63%.

Clustering in data mining is a way of organizing a set of objects in such a way that the objects in same bunch are more comparable and relevant to each other than to those objects in other bunches. In the modern information retrieval system, clustering algorithms are better if they result high quality clusters in efficient time. This study includes analysis of clustering algorithms k-means and enhanced k-means algorithm over the wholesale customers and wine data sets respectively. In this research, the enhanced k-means algorithm is found to be 5% faster for wholesale customers dataset for 4 clusters and 49%, 38% faster when the clusters size is increased to 8 and 13 respectively. The wholesale customers dataset when classified with 18 clusters the speedup was seen to be 29%. Similarly, in the case of wine dataset, the speed up is seen to be 10%, 30%, 49%, and 41% for 3, 8, 13 and 18 clusters respectively. Both of the algorithms are found very similar in terms of the clustering accuracy.

Mushroom is a popular fruit of a much larger fungus that has a high level of protein and a rich source of vitamin B. It aids in the prevention of cancer, weight loss, and immune system enhancement. There are numerous thousands of mushroom species within the world and a few are eatable and a few are noxious due to noteworthy poisons on them. Hence, it is a vital errand to distinguish between eatable and harmful mushrooms. This paper focuses on comparing the performance of Random Forest and Reduced Error Pruning (REP) Tree classification algorithms for the classification of edible and poisonous mushrooms. In this paper, mushroom dataset from UCI machine learning repository has been classified using Random Forest and REP Tree classifiers. The result based on accuracy, precision, recall and F-measure showed that the Random Forest outperformed REP Tree algorithm as it had highest accuracy value of 100%, precision value of 100%, recall value of 100% and F- measure value of 100%. The performance is 100% by using Random Forest, which is found better with respect to REP Tree classifier.