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Zubair Asghar
Institute of Computing and Information Technology (ICIT), Gomal University, D.I.Khan (KP), Pakistan

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Short Biography

Dr. Muhammad Zubair Asghar is an HEC-approved supervisor recognized by the Higher Education Commission (HEC) in Pakistan. His Ph.D. research includes recent issues in opinion mining and sentiment analysis, computational linguistics, and natural language processing. He has more than 50 publications in journals of international repute (JCR and ISI indexed) and has more than 20 years of university teaching and laboratory experience in social computing, text mining, computational linguistics, and opinion mining and sentiment analysis. Currently, he is acting as Reviewer and Academic Editor of different top-tier journals such as PLOS ONE. Furthermore, he has also acted as Special Session Chair (Social Computing) at the BESC 2018 International Conference (Taiwan) and Lead Guest Editor, Special Issue on Social Computing in Health Informatics (Journal of Medical Imaging and Health Informatics, JCR listed).

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Journal article
Published: 19 May 2021 in Sustainability
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The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.

ACS Style

Daniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability 2021, 13, 5691 .

AMA Style

Daniyal Alghazzawi, Syed Hasan, Ghadah Aldabbagh, Mohammed Alhaddad, Areej Malibari, Muhammad Asghar, Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability. 2021; 13 (10):5691.

Chicago/Turabian Style

Daniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. 2021. "Development of Platform Independent Mobile Learning Tool in Saudi Universities." Sustainability 13, no. 10: 5691.

Original research paper
Published: 07 May 2021 in CAAI Transactions on Intelligence Technology
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In the current era of social media, different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events, campaigns, and elections. The acquisition, analysis, and presentation of such content have received considerable attention from opinion‐mining researchers. For this purpose, different supervised and unsupervised techniques have been used. However, they have produced less efficient results, which need to be improved by incorporating additional classifiers with the extended data sets. The authors investigate different supervised machine learning classifiers for classifying the political affiliations of users. For this purpose, a data set of political reviews is acquired from Twitter and annotated with different polarity classes. After pre‐processing, different machine learning classifiers like K‐nearest neighbor, naïve Bayes, support vector machine, extreme gradient boosting, and others, are applied. Experimental results illustrate that support vector machine and extreme gradient boosting have shown promising results for predicting political affiliations.

ACS Style

Hayat Ullah; Bashir Ahmad; Iqra Sana; Anum Sattar; Aurangzeb Khan; Saima Akbar; Muhammad Zubair Asghar. Comparative study for machine learning classifier recommendation to predict political affiliation based on online reviews. CAAI Transactions on Intelligence Technology 2021, 6, 251 -264.

AMA Style

Hayat Ullah, Bashir Ahmad, Iqra Sana, Anum Sattar, Aurangzeb Khan, Saima Akbar, Muhammad Zubair Asghar. Comparative study for machine learning classifier recommendation to predict political affiliation based on online reviews. CAAI Transactions on Intelligence Technology. 2021; 6 (3):251-264.

Chicago/Turabian Style

Hayat Ullah; Bashir Ahmad; Iqra Sana; Anum Sattar; Aurangzeb Khan; Saima Akbar; Muhammad Zubair Asghar. 2021. "Comparative study for machine learning classifier recommendation to predict political affiliation based on online reviews." CAAI Transactions on Intelligence Technology 6, no. 3: 251-264.

Research article
Published: 09 April 2021 in Computational and Mathematical Methods in Medicine
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Nowadays, there is a digital era, where social media sites like Facebook, Google, Twitter, and YouTube are used by the majority of people, generating a lot of textual content. The user-generated textual content discloses important information about people’s personalities, identifying a special type of people known as psychopaths. The aim of this work is to classify the input text into psychopath and nonpsychopath traits. Most of the existing work on psychopath’s detection has been performed in the psychology domain using traditional approaches, like SRPIII technique with limited dataset size. Therefore, it motivates us to build an advanced computational model for psychopath’s detection in the text analytics domain. In this work, we investigate an advanced deep learning technique, namely, attention-based BILSTM for psychopath’s detection with an increased dataset size for efficient classification of the input text into psychopath vs. nonpsychopath classes.

ACS Style

Junaid Asghar; Saima Akbar; Muhammad Zubair Asghar; Bashir Ahmad; Mabrook S. Al-Rakhami; Abdu Gumaei. Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model. Computational and Mathematical Methods in Medicine 2021, 2021, 1 -10.

AMA Style

Junaid Asghar, Saima Akbar, Muhammad Zubair Asghar, Bashir Ahmad, Mabrook S. Al-Rakhami, Abdu Gumaei. Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model. Computational and Mathematical Methods in Medicine. 2021; 2021 ():1-10.

Chicago/Turabian Style

Junaid Asghar; Saima Akbar; Muhammad Zubair Asghar; Bashir Ahmad; Mabrook S. Al-Rakhami; Abdu Gumaei. 2021. "Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model." Computational and Mathematical Methods in Medicine 2021, no. : 1-10.

Journal article
Published: 04 April 2021 in Egyptian Informatics Journal
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Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis. Experimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).

ACS Style

Asad Khattak; Muhammad Zubair Asghar; Zain Ishaq; Waqas Haider Bangyal; Ibrahim A Hameed. Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews. Egyptian Informatics Journal 2021, 1 .

AMA Style

Asad Khattak, Muhammad Zubair Asghar, Zain Ishaq, Waqas Haider Bangyal, Ibrahim A Hameed. Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews. Egyptian Informatics Journal. 2021; ():1.

Chicago/Turabian Style

Asad Khattak; Muhammad Zubair Asghar; Zain Ishaq; Waqas Haider Bangyal; Ibrahim A Hameed. 2021. "Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews." Egyptian Informatics Journal , no. : 1.

Research article
Published: 16 March 2021 in Computational and Mathematical Methods in Medicine
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Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

ACS Style

Hamid Masood Khan; Fazal Masud Khan; Aurangzeb Khan; Muhammad Zubair Asghar; Daniyal M. Alghazzawi. Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique. Computational and Mathematical Methods in Medicine 2021, 2021, 1 -14.

AMA Style

Hamid Masood Khan, Fazal Masud Khan, Aurangzeb Khan, Muhammad Zubair Asghar, Daniyal M. Alghazzawi. Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique. Computational and Mathematical Methods in Medicine. 2021; 2021 ():1-14.

Chicago/Turabian Style

Hamid Masood Khan; Fazal Masud Khan; Aurangzeb Khan; Muhammad Zubair Asghar; Daniyal M. Alghazzawi. 2021. "Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique." Computational and Mathematical Methods in Medicine 2021, no. : 1-14.

Article
Published: 10 March 2021 in Cognitive Computation
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In today’s digital era, the use of online social media networks, such as Google, YouTube, Facebook, and Twitter, permits people to generate a massive amount of textual content. The textual content that is produced by people reveals essential information regarding their personality, with psychopathy being among these distinct personality types. This work was aimed at classifying input texts according to the traits of psychopaths and non-psychopaths. Several studies based on traditional techniques, such as the SRPIII technique, using small-sized datasets have been conducted for the detection of psychopathic behavior. However, the purpose of the current study was to build an effective computational model for the detection of psychopaths in the domain of text analytics and computational intelligence. This study was aimed at developing a technique based on a convolutional neural network + long short-term memory (CNN-LSTM) model by using a deep learning approach to detect psychopaths. A convolutional neural network was used to extract local information from a text, while the long short-term memory was used to extract the contextual dependencies of the text. By combining the advantages of convolutional neural network and long short-term memory, the proposed hybrid CNN-LSTM was able to yield a good classification accuracy of 91.67%. Additionally, a large-sized benchmark dataset was acquired for the effective classification of the given input text into psychopath vs. non-psychopath classes, thereby enabling persons with such personality traits to be identified.

ACS Style

Fahad Mazaed Alotaibi; Muhammad Zubair Asghar; Shakeel Ahmad. A Hybrid CNN-LSTM Model for Psychopathic Class Detection from Tweeter Users. Cognitive Computation 2021, 13, 709 -723.

AMA Style

Fahad Mazaed Alotaibi, Muhammad Zubair Asghar, Shakeel Ahmad. A Hybrid CNN-LSTM Model for Psychopathic Class Detection from Tweeter Users. Cognitive Computation. 2021; 13 (3):709-723.

Chicago/Turabian Style

Fahad Mazaed Alotaibi; Muhammad Zubair Asghar; Shakeel Ahmad. 2021. "A Hybrid CNN-LSTM Model for Psychopathic Class Detection from Tweeter Users." Cognitive Computation 13, no. 3: 709-723.

Journal article
Published: 18 December 2020 in IEEE Access
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In the above article [1] , the affiliation of Fahad Mazaed Alotaibi was incorrectly listed, whereas the authors Shakeel Ahmad and Fahad Mazaed Alotaibi have the same affiliation as mentioned above.

ACS Style

Shakeel Ahmad; Muhammad Zubair Asghar; Fahad Mazaed Alotaibi; Sherafzal Khan. xCorrection to “Classification of Poetry Text Into the Emotional States Using Deep Learning Technique”. IEEE Access 2020, 8, 222255 -222255.

AMA Style

Shakeel Ahmad, Muhammad Zubair Asghar, Fahad Mazaed Alotaibi, Sherafzal Khan. xCorrection to “Classification of Poetry Text Into the Emotional States Using Deep Learning Technique”. IEEE Access. 2020; 8 ():222255-222255.

Chicago/Turabian Style

Shakeel Ahmad; Muhammad Zubair Asghar; Fahad Mazaed Alotaibi; Sherafzal Khan. 2020. "xCorrection to “Classification of Poetry Text Into the Emotional States Using Deep Learning Technique”." IEEE Access 8, no. : 222255-222255.

Journal article
Published: 01 October 2020 in Journal of Medical Imaging and Health Informatics
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In social media, depression identification could be regarded as a complex task because of the complicated nature associated with mental disorders. In recent times, there has been an evolution in this research area with growing popularity of social media platforms as these have become a fundamental part of people's day-to-day life. Social media platforms and their users share a close relationship due to which the users' personal life is reflected in these platforms on several levels. Apart from the associated complexity in recognising mental illnesses via social media platforms, implementing supervised machine learning approaches like deep neural networks is yet to be adopted in a large scale because of the inherent difficulties associated with procuring sufficient quantities of annotated training data. Because of such reasons, we have made effort to identify deep learning model that is most effective from amongst selected architectures with previous successful record in supervised learning methods. The selected model is employed to recognise online users that display depression; since there is limited unstructured text data that could be extracted from Twitter.

ACS Style

Hussain Ahmad; Muhammad Zubair Asghar; Fahad M. Alotaibi; Ibrahim A. Hameed. Applying Deep Learning Technique for Depression Classification in Social Media Text. Journal of Medical Imaging and Health Informatics 2020, 10, 2446 -2451.

AMA Style

Hussain Ahmad, Muhammad Zubair Asghar, Fahad M. Alotaibi, Ibrahim A. Hameed. Applying Deep Learning Technique for Depression Classification in Social Media Text. Journal of Medical Imaging and Health Informatics. 2020; 10 (10):2446-2451.

Chicago/Turabian Style

Hussain Ahmad; Muhammad Zubair Asghar; Fahad M. Alotaibi; Ibrahim A. Hameed. 2020. "Applying Deep Learning Technique for Depression Classification in Social Media Text." Journal of Medical Imaging and Health Informatics 10, no. 10: 2446-2451.

Methodologies and application
Published: 06 September 2020 in Soft Computing
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The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods.

ACS Style

Asad Khattak; Anam Habib; Muhammad Zubair Asghar; Fazli Subhan; Imran Razzak; Ammara Habib. Applying deep neural networks for user intention identification. Soft Computing 2020, 25, 2191 -2220.

AMA Style

Asad Khattak, Anam Habib, Muhammad Zubair Asghar, Fazli Subhan, Imran Razzak, Ammara Habib. Applying deep neural networks for user intention identification. Soft Computing. 2020; 25 (3):2191-2220.

Chicago/Turabian Style

Asad Khattak; Anam Habib; Muhammad Zubair Asghar; Fazli Subhan; Imran Razzak; Ammara Habib. 2020. "Applying deep neural networks for user intention identification." Soft Computing 25, no. 3: 2191-2220.

Conference paper
Published: 14 August 2020 in Communications in Computer and Information Science
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The prediction of judicial decisions based on historical datasets in the legal domain is a challenging task. To answer the question about how the court will render a decision in a particular case has remained an important issue. Prior studies conducted on the prediction of judicial case decisions have datasets with limited size by experimenting less efficient set of predictors variables applied to different machine learning classifiers. In this work, we investigate and apply more efficient sets of predictors variables with a machine learning classifier over a large size legal dataset for court judgment prediction. Experimental results are encouraging and depict that incorporation of feature selection technique has significantly improved the performance of predictive classifier.

ACS Style

Anwar Ullah; Muhammad Zubair Asghar; Anam Habib; Saiqa Aleem; Fazal Masud Kundi; Asad Masood Khattak. Optimizing the Efficiency of Machine Learning Techniques. Communications in Computer and Information Science 2020, 553 -567.

AMA Style

Anwar Ullah, Muhammad Zubair Asghar, Anam Habib, Saiqa Aleem, Fazal Masud Kundi, Asad Masood Khattak. Optimizing the Efficiency of Machine Learning Techniques. Communications in Computer and Information Science. 2020; ():553-567.

Chicago/Turabian Style

Anwar Ullah; Muhammad Zubair Asghar; Anam Habib; Saiqa Aleem; Fazal Masud Kundi; Asad Masood Khattak. 2020. "Optimizing the Efficiency of Machine Learning Techniques." Communications in Computer and Information Science , no. : 553-567.

Special issue paper
Published: 03 August 2020 in Software: Practice and Experience
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In the competing era of online industries, understanding customer feedback and satisfaction is one of the important concern for any business organization. The well‐known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing customer feedback is beneficial, as it provides an advantage way of unveiling customer interests. The proposed system, namely Senti‐eSystem , aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. The proposed approach initially deploys a Bidirectional Long Short Term Memory with attention mechanism to predict the sentiment polarity that is positive and negative, followed by Fuzzy logic approach to determine the customer satisfaction level, which further strengthens the capabilities of the proposed approach. The system achieves an accuracy of 92.86%, outperforming the previous state‐of‐art lexicon‐based approaches. Moreover, the effectiveness of the proposed system is also validated by applying the statistical test.

ACS Style

Muhammad Zubair Asghar; Fazli Subhan; Hussain Ahmad; Wazir Zada Khan; Saqib Hakak; Thippa Reddy Gadekallu; Mamoun Alazab. Senti‐eSystem : A sentiment‐based eSystem ‐using hybridized fuzzy and deep neural network for measuring customer satisfaction. Software: Practice and Experience 2020, 51, 571 -594.

AMA Style

Muhammad Zubair Asghar, Fazli Subhan, Hussain Ahmad, Wazir Zada Khan, Saqib Hakak, Thippa Reddy Gadekallu, Mamoun Alazab. Senti‐eSystem : A sentiment‐based eSystem ‐using hybridized fuzzy and deep neural network for measuring customer satisfaction. Software: Practice and Experience. 2020; 51 (3):571-594.

Chicago/Turabian Style

Muhammad Zubair Asghar; Fazli Subhan; Hussain Ahmad; Wazir Zada Khan; Saqib Hakak; Thippa Reddy Gadekallu; Mamoun Alazab. 2020. "Senti‐eSystem : A sentiment‐based eSystem ‐using hybridized fuzzy and deep neural network for measuring customer satisfaction." Software: Practice and Experience 51, no. 3: 571-594.

Review
Published: 01 July 2020 in Open Computer Science
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The day-to-day use of digital devices with Internet access, such as tablets and smartphones, has increased exponentially in recent years and this has had a consequent effect on the usage of the Internet and social media networks. When using social networks, people share personal data that is broadcast between users, which provides useful information for organizations. This means that characterizing users through their social media activity is an emerging research area in the field of Natural Language Processing (NLP) and this paper will present a review of how personality can be detected using online content.Approach A systematic literature review identified 30 papers published between 2007 and 2019, while particular inclusion and exclusion criteria were used to select the most relevant articles.Outcomes This review describes a variety of challenges and trends, as well as providing ideas for the direction of future research. In addition, personality trait identification and techniques were classified into different types, including deep learning, machine learning (ML) and semi-supervised/hybrid.Implications This paper’s outcomes will not only facilitate insight into the various personality types and models but will also provide knowledge about the relevant detection techniques.Novelty While prior studies have conducted literature reviews in the personality trait detection field, the systematic literature review in this paper provides specific answers to the proposed research questions. This is novel to this field as this particular type of study has not been conducted before.

ACS Style

Hussain Ahmad; Muhammad Zubair Asghar; Alam Sher Khan; Anam Habib. A Systematic Literature Review of Personality Trait Classification from Textual Content. Open Computer Science 2020, 10, 175 -193.

AMA Style

Hussain Ahmad, Muhammad Zubair Asghar, Alam Sher Khan, Anam Habib. A Systematic Literature Review of Personality Trait Classification from Textual Content. Open Computer Science. 2020; 10 (1):175-193.

Chicago/Turabian Style

Hussain Ahmad; Muhammad Zubair Asghar; Alam Sher Khan; Anam Habib. 2020. "A Systematic Literature Review of Personality Trait Classification from Textual Content." Open Computer Science 10, no. 1: 175-193.

Review article
Published: 15 May 2020 in Egyptian Informatics Journal
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The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis.

ACS Style

Asad Khattak; Muhammad Zubair Asghar; Anam Saeed; Ibrahim A. Hameed; Syed Asif Hassan; Shakeel Ahmad. A survey on sentiment analysis in Urdu: A resource-poor language. Egyptian Informatics Journal 2020, 22, 53 -74.

AMA Style

Asad Khattak, Muhammad Zubair Asghar, Anam Saeed, Ibrahim A. Hameed, Syed Asif Hassan, Shakeel Ahmad. A survey on sentiment analysis in Urdu: A resource-poor language. Egyptian Informatics Journal. 2020; 22 (1):53-74.

Chicago/Turabian Style

Asad Khattak; Muhammad Zubair Asghar; Anam Saeed; Ibrahim A. Hameed; Syed Asif Hassan; Shakeel Ahmad. 2020. "A survey on sentiment analysis in Urdu: A resource-poor language." Egyptian Informatics Journal 22, no. 1: 53-74.

Journal article
Published: 14 April 2020 in IEEE Access
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The classification of emotional states from poetry or formal text has received less attention by the experts of computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an emotional state classification system for poetry text is proposed using the latest and cutting edge technology of Artificial Intelligence, called Deep Learning. For this purpose, an attention-based C-BiLSTM model is implemented on the poetry corpus. The proposed approach classifies the text of poetry into different emotional states, like love, joy, hope, sadness, anger, etc. Different experiments are conducted to evaluate the efficiency of the proposed system as compared to other state-of-art methods as well as machine learning and deep learning methods. Experimental results depict that the proposed model outperformed the baselines studies with 88% accuracy. Furthermore, the analysis of the statistical experiment also validates the performance of the proposed approach.

ACS Style

Shakeel Ahmad; Muhammad Zubair Asghar; Fahad Mazaed Alotaibi; Sherafzal Khan. Classification of Poetry Text Into the Emotional States Using Deep Learning Technique. IEEE Access 2020, 8, 73865 -73878.

AMA Style

Shakeel Ahmad, Muhammad Zubair Asghar, Fahad Mazaed Alotaibi, Sherafzal Khan. Classification of Poetry Text Into the Emotional States Using Deep Learning Technique. IEEE Access. 2020; 8 (99):73865-73878.

Chicago/Turabian Style

Shakeel Ahmad; Muhammad Zubair Asghar; Fahad Mazaed Alotaibi; Sherafzal Khan. 2020. "Classification of Poetry Text Into the Emotional States Using Deep Learning Technique." IEEE Access 8, no. 99: 73865-73878.

Journal article
Published: 19 February 2020 in Entertainment Computing
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Artificial Intelligence in the form of machine learning is employed in games to control non-human computer-players, agents or bots. However, most of these games such as Atari took place in 2D environments that were not fully observable to the agents. Currently, it is of extreme significance to employ such machine learning techniques and methods in 3D environments such as Doom. Therefore, In this paper, we train agents on the health gathering scenario of the classical first-person shooter game Doom by first presenting the Direct Future Prediction to train an agent that uses a simple architecture with no additional supervisory signals, then differentiate and compare the performance of the agents trained by using several different machine learning techniques, and the AI reinforcement learning platform ‘VizDoom’, a 3D partially observable environment, with interesting enhanced properties that makes agents to stand out from inbuilt AI agents and human players. We have continued to use computer games as a benchmark for the performance of AI as having been so successful in the past. We also compared the results of our findings to conclude the performance of the agents trained with different machine learning techniques. The agents performed well against both human players and inbuilt game agents.

ACS Style

Adil Khan; Muhammad Naeema; Muhammad Zubair Asghar; Aziz Ud Din; Atif Khand. Playing first-person shooter games with machine learning techniques and methods using the VizDoom Game-AI research platform. Entertainment Computing 2020, 34, 100357 .

AMA Style

Adil Khan, Muhammad Naeema, Muhammad Zubair Asghar, Aziz Ud Din, Atif Khand. Playing first-person shooter games with machine learning techniques and methods using the VizDoom Game-AI research platform. Entertainment Computing. 2020; 34 ():100357.

Chicago/Turabian Style

Adil Khan; Muhammad Naeema; Muhammad Zubair Asghar; Aziz Ud Din; Atif Khand. 2020. "Playing first-person shooter games with machine learning techniques and methods using the VizDoom Game-AI research platform." Entertainment Computing 34, no. : 100357.

Journal
Published: 01 January 2020 in International Journal of Computational Intelligence Systems
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In recent years, the boom in social media sites such as Facebook and Twitter has brought people together for the sharing of opinions, sentiments, emotions, and experiences about products, events, politics, and other topics. In particular, sentiment-based applications are growing in popularity among individuals and businesses for the making of purchase decisions. Fuzzy-based sentiment analysis aims at classifying customer sentiment at a fine-grained level. This study deals with the development of a fuzzy-based sentiment analysis by extending fuzzy hedges and rule-sets for a more efficient classification of customer sentiment and satisfaction. Prior studies have used a limited number of linguistic hedges and polarity classes in their rule-sets, resulting in the degraded efficiency of their fuzzy-based sentiment analysis systems. The proposed analysis of the current study classifies customer reviews using fuzzy linguistic hedges and an extended rule-set with seven sentiment analysis classes, namely extremely positive, very positive, positive, neutral, negative, very negative, and extremely negative. Then, a fuzzy logic system is applied to measure customer satisfaction at a fine-grained level. The experimental results demonstrate that the proposed analysis has an improved performance over the baseline works.

ACS Style

Asad Khattak; Waqas Tariq Paracha; Muhammad Zubair Asghar; Nosheen Jillani; Umair Younis; Furqan Khan Saddozai; Ibrahim A. Hameed. Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges. International Journal of Computational Intelligence Systems 2020, 13, 744 .

AMA Style

Asad Khattak, Waqas Tariq Paracha, Muhammad Zubair Asghar, Nosheen Jillani, Umair Younis, Furqan Khan Saddozai, Ibrahim A. Hameed. Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges. International Journal of Computational Intelligence Systems. 2020; 13 (1):744.

Chicago/Turabian Style

Asad Khattak; Waqas Tariq Paracha; Muhammad Zubair Asghar; Nosheen Jillani; Umair Younis; Furqan Khan Saddozai; Ibrahim A. Hameed. 2020. "Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges." International Journal of Computational Intelligence Systems 13, no. 1: 744.

Journal article
Published: 01 January 2020 in Computers, Materials & Continua
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The feedback collection and analysis has remained an important subject matter for long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

ACS Style

Yun Wang; Fazli Subhan; Shahaboddin Shamshirband; Muhammad Zubair Asghar; Ikram Ullah; Ammara Habib. Fuzzy-based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction. Computers, Materials & Continua 2020, 62, 631 -655.

AMA Style

Yun Wang, Fazli Subhan, Shahaboddin Shamshirband, Muhammad Zubair Asghar, Ikram Ullah, Ammara Habib. Fuzzy-based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction. Computers, Materials & Continua. 2020; 62 (2):631-655.

Chicago/Turabian Style

Yun Wang; Fazli Subhan; Shahaboddin Shamshirband; Muhammad Zubair Asghar; Ikram Ullah; Ammara Habib. 2020. "Fuzzy-based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction." Computers, Materials & Continua 62, no. 2: 631-655.

Journal article
Published: 01 January 2020 in Computers, Materials & Continua
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Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

ACS Style

Muhammad Zubair Asghar; Fazli Subhan; Muhammad Imran; Fazal Masud Kundi; Adil Khan; Shahboddin Shamshirband; Amir Mosavi; Annamaria R. Varkonyi Koczy; Peter Csiba. Performance Evaluation Of Supervised Machine Learning Techniques For Efficient Detection Of Emotions From Online Content. Computers, Materials & Continua 2020, 63, 1093 -1118.

AMA Style

Muhammad Zubair Asghar, Fazli Subhan, Muhammad Imran, Fazal Masud Kundi, Adil Khan, Shahboddin Shamshirband, Amir Mosavi, Annamaria R. Varkonyi Koczy, Peter Csiba. Performance Evaluation Of Supervised Machine Learning Techniques For Efficient Detection Of Emotions From Online Content. Computers, Materials & Continua. 2020; 63 (3):1093-1118.

Chicago/Turabian Style

Muhammad Zubair Asghar; Fazli Subhan; Muhammad Imran; Fazal Masud Kundi; Adil Khan; Shahboddin Shamshirband; Amir Mosavi; Annamaria R. Varkonyi Koczy; Peter Csiba. 2020. "Performance Evaluation Of Supervised Machine Learning Techniques For Efficient Detection Of Emotions From Online Content." Computers, Materials & Continua 63, no. 3: 1093-1118.

Special issue article
Published: 22 November 2019 in Transactions on Emerging Telecommunications Technologies
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Received Signal Strength Indicator (RSSI) is the measurement of the power in the radio signal and a parameter for distance‐based measurements. Bluetooth Low Energy (BLE) is an advance implementation for Internet of Things (IoT). BLE beacons‐based indoor positioning systems provide an easy and energy‐efficient, low deployment cost solution for wide variety of applications including smart phones. This paper presents an in‐depth experimental study of BLE RSSI in a dense indoor environment. Due to noise, fluctuations in RSSI occur, which produces distance estimation error, which ultimately affect position estimation accuracy. The main objective of this paper is to know the variations in RSSI and develop a radio propagation model in order to minimize the distance estimation and position estimation error. Based on our real‐time experimental analysis using BLE modules, there is an average 1.32‐m position estimation error in the presence of 10‐dBm variation in RSSI. Moreover, we also observed that environmental specific radio propagation constants greatly affect distance and position estimation accuracy in BLE modules.

ACS Style

Fazli Subhan; Asfandyar Khan; Sajid Saleem; Shakeel Ahmed; Muhammad Imran; Zubair Asghar; Javed Iqbal Bangash. Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation. Transactions on Emerging Telecommunications Technologies 2019, 1 .

AMA Style

Fazli Subhan, Asfandyar Khan, Sajid Saleem, Shakeel Ahmed, Muhammad Imran, Zubair Asghar, Javed Iqbal Bangash. Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation. Transactions on Emerging Telecommunications Technologies. 2019; ():1.

Chicago/Turabian Style

Fazli Subhan; Asfandyar Khan; Sajid Saleem; Shakeel Ahmed; Muhammad Imran; Zubair Asghar; Javed Iqbal Bangash. 2019. "Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation." Transactions on Emerging Telecommunications Technologies , no. : 1.

Original research
Published: 17 October 2019 in Journal of Ambient Intelligence and Humanized Computing
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The widespread propagation of numerous rumors and fake news have seriously threatened the credibility of microblogs. Previous works often focused on maintaining the previous state without considering the subsequent context information. Furthermore, most of the early works have used classical feature representation schemes followed by a classifier. We investigate the rumor detection problem by exploring different Deep Learning models with emphasis on considering the contextual information in both directions: forward and backward, in a given text. The proposed system is based on Bidirectional Long Short-Term Memory with Convolutional Neural Network, effectively classifying the tweet into rumors and non-rumors. Experimental results show that the proposed method outperformed the baseline methods with 86.12% accuracy. Furthermore, the statistical analysis also shows the effectiveness of the proposed model than the comparing methods.

ACS Style

Muhammad Zubair Asghar; Ammara Habib; Anam Habib; Adil Khan; Rehman Ali; Asad Khattak. Exploring deep neural networks for rumor detection. Journal of Ambient Intelligence and Humanized Computing 2019, 12, 4315 -4333.

AMA Style

Muhammad Zubair Asghar, Ammara Habib, Anam Habib, Adil Khan, Rehman Ali, Asad Khattak. Exploring deep neural networks for rumor detection. Journal of Ambient Intelligence and Humanized Computing. 2019; 12 (4):4315-4333.

Chicago/Turabian Style

Muhammad Zubair Asghar; Ammara Habib; Anam Habib; Adil Khan; Rehman Ali; Asad Khattak. 2019. "Exploring deep neural networks for rumor detection." Journal of Ambient Intelligence and Humanized Computing 12, no. 4: 4315-4333.