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| 1 | Text Sentiment Analysis Using Frequency-Based Vigorous Features显示文摘Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics. | Abdul Razzaq Muhammad Asim Zulqrnain Ali Salman Qadri Imran Mumtaz Dost Muhammad Khan Qasim Niaz | 2019 | China Communications2019，16，12： | 2 |
| 2 | Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments显示文摘The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio(SNR).The proposed method utilizes the robust measures of location i.e.,the“Median”as well as the measures of variation i.e.,“Median absolute deviation(MAD)and Interquartile range(IQR)”in the SNR.By this way,two independent robust signal-to-noise ratios have been proposed.The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio(RSNR).The results obtained via the proposed method are compared with wellknown gene/feature selection methods on the basis of performance metric i.e.,classification error rate.A total of 5 gene expression datasets have been used in this study.Different subsets of informative genes are selected by the proposed and all the other methods included in the study,and their efficacy in terms of classification is investigated by using the classifier models such as support vector machine(SVM),Random forest(RF)and k-nearest neighbors(k-NN).The results of the analysis reveal that the proposed method(RSNR)produces minimum error rates than all the other competing feature selection methods in majority of the cases.For further assessment of the method,a detailed simulation study is also conducted. | Muhammad Hamraz Dost Muhammad Khan Naz Gul Amjad Ali Zardad Khan Shafiq Ahmad Mejdal Alqahtani Akber Abid Gardezi Muhammad Shafiq | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 3 | Modelling Insurance Losses with a New Family of Heavy-Tailed Distributions显示文摘The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk issues.In this article,we introduce a new class of heavy-tailed distributions useful for modeling data in financial sciences.A specific sub-model form of our suggested family,named as a new extended heavy-tailed Weibull distribution is examined in detail.Some basic characterizations,including quantile function and raw moments have been derived.The estimates of the unknown parameters of the new model are obtained via the maximum likelihood estimation method.To judge the performance of the maximum likelihood estimators,a simulation analysis is performed in detail.Furthermore,some important actuarial measures such as value at risk and tail value at risk are also computed.A simulation study based on these actuarial measures is conducted to exhibit empirically that the proposed model is heavy-tailed.The usefulness of the proposed family is illustrated by means of an application to a heavy-tailed insurance loss data set.The practical application shows that the proposed model is more flexible and efficient than the other six competing models including(i)the two-parameter models Weibull,Lomax and Burr-XII distributions(ii)the three-parameter distributions Marshall-Olkin Weibull and exponentiated Weibull distributions,and(iii)a well-known four-parameter Kumaraswamy Weibull distribution. | Muhammad Arif Dost Muhammad Khan Saima Khan Khosa Muhammad Aamir Adnan Aslam Zubair Ahmad Wei Gao | 2021 | Computers, Materials & Continua2021，，1： | 0 |
| 4 | Analyzing COVID-2019 Impact on Mental Health Through Social Media Forum显示文摘This study aims to identify the potential association of mental health and social media forum during the outbreak of COVID-19 pandemic.COVID-19 brings a lot of challenges to government globally.Among the different strategies the most extensively adopted ones were lockdown,social distancing,and isolation among others.Most people with no mental illness history have been found with high risk of distress and psychological discomfort due to anxiety of being infected with the virus.Panic among people due to COVID-19 spread faster than the disease itself.The misinformation and excessive usage of social media in this pandemic era have adversely affected mental health across the world.Due to limited historical data,psychiatrists are finding it difficult to cure the mental illness of people resulting from the pandemic repercussion,fueled by social media forum.In this study the methodology used for data extraction is by considering the implications of social network platforms(such as Reddit)and levering the capabilities of a semi-supervised co-training technique-based use of Naïve Bayes(NB),Random Forest(RF),and Support Vector Machine(SVM)classifiers.The experimental results shows the efficacy of the proposed methodology to identify the mental illness level(such as anxiety,bipolar disorder,depression,PTSD,schizophrenia,and OCD)of those who are in anxious of being infected with this virus.We observed 1 to 5%improvement in the classification decision through the proposed method as compared to state-of-the-art classifiers. | Huma Muhammad Khalid Sohail Nadeem Akhtar Dost Muhammad Humaira Afzal Muhammad Rafiq Mufti Shahid Hussain Mansoor Ahmed | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 5 | Analyzing COVID-19 Impact on the Researchers Productivity through Their Perceptions显示文摘Context:Since the end of 2019,the COVID-19 pandemic had a worst impact on world’s economy,healthcare,and education.There are several aspects where the impact of COVID-19 could be visualized.Among these,one aspect is the productivity of researcher,which plays a significant role in the success of an organization.Problem:There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization.Method:We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions.Our study aims to find out the impact of COVID-19 on the researcher’s productivity that are working in different fields.In this study,we conduct a questionnaire-based analysis,which included feedback of 152 researchers of certain domains.These researchers are currently involved in different research activities.Subsequently,we perform a statistical analysis to analyze the collected responses and report the findings.Findings:The results indicate the substantial impact of COVID-19 pandemics on the researcher’s productivity in terms of mental disturbance,lack of regular meetings,and field visits for the collection of primary data.Conclusion:Finally,it is concluded that researcher’s daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion.These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization. | Syeda Javeria Shoukat Humaira Afzal Muhammad Rafiq Mufti Muhammad Khalid Sohail Dost Muhammad Khan Nadeem Akhtar Shahid Hussain Mansoor Ahmed | 2021 | Computers, Materials & Continua2021，，5： | 0 |
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