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Dr. Arshad Ahmad
Pak-Austria Fachhochschule Institute of Applied Sciences and Technology

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0 Requirements Engineering
0 Requirements Gathering
0 Text Analytics
0 Social Media Analytics
0 Opinion Mining

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Short Biography

Arshad Ahmad received the MS Software Engineering degree from Blekinge Tekniska Högskola (BTH), Sweden in 2008. He worked as a Research Assistant at Fraunhofer Institute of Experimental Software Engineering (IESE), Germany and Institute of Computer Technology, Vienna University of Technology, Austria respectively during the years 2010-2014. Afterwards, he received the PhD degree in Computer Science & Technology (specialization in Software Engineering) from Beijing Institute of Technology, China in 2018. He worked as an Assistant Professor of Software Engineering/Computer Science at the City University of Science & Information Technology, Peshawar, and University of Swabi, Swabi during the years August 2018-October 2020. In October 2020 he started working as an Assistant Professor of Software Engineering & Computer Science at Department of IT & CS, Pak Austria Fachhochschule: Institute of Applied Sciences & Technology, Haripur, Pakistan. He has published several research papers in well reputed peer reviewed international journals and conferences. His current research interests include Requirements Engineering, Software Engineering, Software Quality Management, Software Analytics, Text Mining, among others.

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Journal article
Published: 24 May 2021 in Sustainability
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Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities remain unknown; this is of high concern given their growing use in security-sensitive applications such as question answering and machine translation in the aspects of sustainability. In this study, we demonstrate that NRE models are inherently vulnerable to adversarially crafted text that contains imperceptible modifications of the original but can mislead the target NRE model. Specifically, we propose a novel sustainable term frequency-inverse document frequency (TFIDF) based black-box adversarial attack to evaluate the robustness of state-of-the-art CNN, CGN, LSTM, and BERT-based models on two benchmark RE datasets. Compared with white-box adversarial attacks, black-box attacks impose further constraints on the query budget; thus, efficient black-box attacks remain an open problem. By applying TFIDF to the correctly classified sentences of each class label in the test set, the proposed query-efficient method achieves a reduction of up to 70% in the number of queries to the target model for identifying important text items. Based on these items, we design both character- and word-level perturbations to generate adversarial examples. The proposed attack successfully reduces the accuracy of six representative models from an average F1 score of 80% to below 20%. The generated adversarial examples were evaluated by humans and are considered semantically similar. Moreover, we discuss defense strategies that mitigate such attacks, and the potential countermeasures that could be deployed in order to improve sustainability of the proposed scheme.

ACS Style

Ijaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability 2021, 13, 5892 .

AMA Style

Ijaz Haq, Zahid Khan, Arshad Ahmad, Bashir Hayat, Asif Khan, Ye-Eun Lee, Ki-Il Kim. Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks. Sustainability. 2021; 13 (11):5892.

Chicago/Turabian Style

Ijaz Haq; Zahid Khan; Arshad Ahmad; Bashir Hayat; Asif Khan; Ye-Eun Lee; Ki-Il Kim. 2021. "Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks." Sustainability 13, no. 11: 5892.

Review article
Published: 07 April 2021 in Complexity
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Context. Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. Method. Conducted a systematic mapping study (SMS) on election predictions on Twitter and provided empirical evidence for the work published between January 2010 and January 2021. Results. This research identified 787 studies related to election predictions on Twitter. 98 primary studies were selected after defining and implementing several inclusion/exclusion criteria. The results show that most of the studies implemented sentiment analysis (SA) followed by volume-based and social network analysis (SNA) approaches. The majority of the studies employed supervised learning techniques, subsequently, lexicon-based approach SA, volume-based, and unsupervised learning. Besides this, 18 types of dictionaries were identified. Elections of 28 countries were analyzed, mainly USA (28%) and Indian (25%) elections. Furthermore, the results revealed that 50% of the primary studies used English tweets. The demographic data showed that academic organizations and conference venues are the most active. Conclusion. The evolution of the work published in the past 11 years shows that most of the studies employed SA. The implementation of SNA techniques is lower as compared to SA. Appropriate political labelled datasets are not available, especially in languages other than English. Deep learning needs to be employed in this domain to get better predictions.

ACS Style

Asif Khan; Huaping Zhang; Nada Boudjellal; Arshad Ahmad; Jianyun Shang; Lin Dai; Bashir Hayat. Election Prediction on Twitter: A Systematic Mapping Study. Complexity 2021, 2021, 1 -27.

AMA Style

Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Jianyun Shang, Lin Dai, Bashir Hayat. Election Prediction on Twitter: A Systematic Mapping Study. Complexity. 2021; 2021 ():1-27.

Chicago/Turabian Style

Asif Khan; Huaping Zhang; Nada Boudjellal; Arshad Ahmad; Jianyun Shang; Lin Dai; Bashir Hayat. 2021. "Election Prediction on Twitter: A Systematic Mapping Study." Complexity 2021, no. : 1-27.

Research article
Published: 13 March 2021 in Complexity
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The web is being loaded daily with a huge volume of data, mainly unstructured textual data, which increases the need for information extraction and NLP systems significantly. Named-entity recognition task is a key step towards efficiently understanding text data and saving time and effort. Being a widely used language globally, English is taking over most of the research conducted in this field, especially in the biomedical domain. Unlike other languages, Arabic suffers from lack of resources. This work presents a BERT-based model to identify biomedical named entities in the Arabic text data (specifically disease and treatment named entities) that investigates the effectiveness of pretraining a monolingual BERT model with a small-scale biomedical dataset on enhancing the model understanding of Arabic biomedical text. The model performance was compared with two state-of-the-art models (namely, AraBERT and multilingual BERT cased), and it outperformed both models with 85% F1-score.

ACS Style

Nada Boudjellal; Huaping Zhang; Asif Khan; Arshad Ahmad; Rashid Naseem; Jianyun Shang; Lin Dai. ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition. Complexity 2021, 2021, 1 -6.

AMA Style

Nada Boudjellal, Huaping Zhang, Asif Khan, Arshad Ahmad, Rashid Naseem, Jianyun Shang, Lin Dai. ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition. Complexity. 2021; 2021 ():1-6.

Chicago/Turabian Style

Nada Boudjellal; Huaping Zhang; Asif Khan; Arshad Ahmad; Rashid Naseem; Jianyun Shang; Lin Dai. 2021. "ABioNER: A BERT-Based Model for Arabic Biomedical Named-Entity Recognition." Complexity 2021, no. : 1-6.

Research article
Published: 30 January 2021 in Security and Communication Networks
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The interest in Facial Expression Recognition (FER) is increasing day by day due to its practical and potential applications, such as human physiological interaction diagnosis and mental disease detection. This area has received much attention from the research community in recent years and achieved remarkable results; however, a significant improvement is required in spatial problems. This research work presents a novel framework and proposes an effective and robust solution for FER under an unconstrained environment; it also helps us to classify facial images in the client/server model along with preserving privacy. There are a lot of cryptography techniques available but they are computationally expensive; on the other side, we have implemented a lightweight method capable of ensuring secure communication with the help of randomization. Initially, we perform preprocessing techniques to encounter the unconstrained environment. Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques.

ACS Style

Asad Ullah; Jing Wang; M. Shahid Anwar; Arshad Ahmad; Shah Nazir; Habib Ullah Khan; Zesong Fei. Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition. Security and Communication Networks 2021, 2021, 1 -12.

AMA Style

Asad Ullah, Jing Wang, M. Shahid Anwar, Arshad Ahmad, Shah Nazir, Habib Ullah Khan, Zesong Fei. Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition. Security and Communication Networks. 2021; 2021 ():1-12.

Chicago/Turabian Style

Asad Ullah; Jing Wang; M. Shahid Anwar; Arshad Ahmad; Shah Nazir; Habib Ullah Khan; Zesong Fei. 2021. "Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition." Security and Communication Networks 2021, no. : 1-12.

Journal article
Published: 14 January 2021 in Electronics
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Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.

ACS Style

Rashid Naseem; Zain Shaukat; Muhammad Irfan; Muhammad Arif Shah; Arshad Ahmad; Fazal Muhammad; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu; Adel Sulaiman. Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction. Electronics 2021, 10, 168 .

AMA Style

Rashid Naseem, Zain Shaukat, Muhammad Irfan, Muhammad Arif Shah, Arshad Ahmad, Fazal Muhammad, Adam Glowacz, Larisa Dunai, Jose Antonino-Daviu, Adel Sulaiman. Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction. Electronics. 2021; 10 (2):168.

Chicago/Turabian Style

Rashid Naseem; Zain Shaukat; Muhammad Irfan; Muhammad Arif Shah; Arshad Ahmad; Fazal Muhammad; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu; Adel Sulaiman. 2021. "Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction." Electronics 10, no. 2: 168.

Research article
Published: 03 December 2020 in Scientific Programming
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Segmentation of cursive text remains the challenging phase in the recognition of text. In OCR systems, the recognition accuracy of text is directly dependent on the quality of segmentation. In cursive text OCR systems, the segmentation of handwritten Urdu language text is a complex task because of the context sensitivity and diagonality of the text. This paper presents a line segmentation algorithm for Urdu handwritten and printed text and subsequently to ligatures. In the proposed technique, the counting pixel approach is employed for modified header and baseline detection, in which the system first removes the skewness of the text page, and then the page is converted into lines and ligatures. The algorithm is evaluated on manually generated Urdu printed and handwritten dataset. The proposed algorithm is tested separately on handwritten and printed text, showing 96.7% and 98.3% line accuracy, respectively. Furthermore, the proposed line segmentation algorithm correctly extracts the lines when tested on Arabic text.

ACS Style

Saud Malik; Ahthasham Sajid; Arshad Ahmad; Ahmad Almogren; Bashir Hayat; Muhammad Awais; Kyong Hoon Kim. An Efficient Skewed Line Segmentation Technique for Cursive Script OCR. Scientific Programming 2020, 2020, 1 -12.

AMA Style

Saud Malik, Ahthasham Sajid, Arshad Ahmad, Ahmad Almogren, Bashir Hayat, Muhammad Awais, Kyong Hoon Kim. An Efficient Skewed Line Segmentation Technique for Cursive Script OCR. Scientific Programming. 2020; 2020 ():1-12.

Chicago/Turabian Style

Saud Malik; Ahthasham Sajid; Arshad Ahmad; Ahmad Almogren; Bashir Hayat; Muhammad Awais; Kyong Hoon Kim. 2020. "An Efficient Skewed Line Segmentation Technique for Cursive Script OCR." Scientific Programming 2020, no. : 1-12.

Research article
Published: 30 November 2020 in Complexity
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The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD (Urdu Slang and Abusive words Detection), a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the nonavailability of the standard dataset, we also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models.

ACS Style

Nauman Ul Haq; Mohib Ullah; Rafiullah Khan; Arshad Ahmad; Ahmad Almogren; Bashir Hayat; Bushra Shafi. USAD: An Intelligent System for Slang and Abusive Text Detection in PERSO-Arabic-Scripted Urdu. Complexity 2020, 2020, 1 -7.

AMA Style

Nauman Ul Haq, Mohib Ullah, Rafiullah Khan, Arshad Ahmad, Ahmad Almogren, Bashir Hayat, Bushra Shafi. USAD: An Intelligent System for Slang and Abusive Text Detection in PERSO-Arabic-Scripted Urdu. Complexity. 2020; 2020 ():1-7.

Chicago/Turabian Style

Nauman Ul Haq; Mohib Ullah; Rafiullah Khan; Arshad Ahmad; Ahmad Almogren; Bashir Hayat; Bushra Shafi. 2020. "USAD: An Intelligent System for Slang and Abusive Text Detection in PERSO-Arabic-Scripted Urdu." Complexity 2020, no. : 1-7.

Research article
Published: 30 November 2020 in Complexity
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Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. These techniques include Credal Decision Tree (CDT), Cost-Sensitive Decision Forest (CS-Forest), Decision Stump (DS), Forest by Penalizing Attributes (Forest-PA), Hoeffding Tree (HT), Decision Tree (J48), Logistic Model Tree (LMT), Random Forest (RF), Random Tree (RT), and REP-Tree (REP-T). Performance of each technique is evaluated using different measures, i.e., mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), root relative squared error (RRSE), specificity, precision, recall, F-measure (FM), G-measure (GM), Matthew’s correlation coefficient (MCC), and accuracy. The overall outcomes of this paper suggested RF technique by producing best results in terms of reducing error rates as well as increasing accuracy on five datasets, i.e., AR3, PC1, PC2, PC3, and PC4. The average accuracy achieved by RF is 90.2238%. The comprehensive outcomes of this study can be used as a reference point for other researchers. Any assertion concerning the enhancement in prediction through any new model, technique, or framework can be benchmarked and verified.

ACS Style

Rashid Naseem; Bilal Khan; Arshad Ahmad; Ahmad Almogren; Saima Jabeen; Bashir Hayat; Muhammad Arif Shah. Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects. Complexity 2020, 2020, 1 -21.

AMA Style

Rashid Naseem, Bilal Khan, Arshad Ahmad, Ahmad Almogren, Saima Jabeen, Bashir Hayat, Muhammad Arif Shah. Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects. Complexity. 2020; 2020 ():1-21.

Chicago/Turabian Style

Rashid Naseem; Bilal Khan; Arshad Ahmad; Ahmad Almogren; Saima Jabeen; Bashir Hayat; Muhammad Arif Shah. 2020. "Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects." Complexity 2020, no. : 1-21.

Research article
Published: 27 November 2020 in Wireless Communications and Mobile Computing
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Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

ACS Style

Shahzad Ashraf; Omar Alfandi; Arshad Ahmad; Asad Masood Khattak; Bashir Hayat; Kyong Hoon Kim; Ayaz Ullah. Bodacious-Instance Coverage Mechanism for Wireless Sensor Network. Wireless Communications and Mobile Computing 2020, 2020, 1 -11.

AMA Style

Shahzad Ashraf, Omar Alfandi, Arshad Ahmad, Asad Masood Khattak, Bashir Hayat, Kyong Hoon Kim, Ayaz Ullah. Bodacious-Instance Coverage Mechanism for Wireless Sensor Network. Wireless Communications and Mobile Computing. 2020; 2020 ():1-11.

Chicago/Turabian Style

Shahzad Ashraf; Omar Alfandi; Arshad Ahmad; Asad Masood Khattak; Bashir Hayat; Kyong Hoon Kim; Ayaz Ullah. 2020. "Bodacious-Instance Coverage Mechanism for Wireless Sensor Network." Wireless Communications and Mobile Computing 2020, no. : 1-11.

Earlycite article
Published: 11 November 2020 in Library Hi Tech
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PurposeThe World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few years. Electronic media, i.e., television channels, magazines and newspapers, have started publishing news online. This online information is prompt to be disappeared because of short life-span and imperative to be archived for the long-term and future generations. This paper presents a content-based similarity measure based on the headings of the news articles for linking digital news stories published in various newspapers during the preservation process that helps to ensure future accessibility.Design/methodology/approachTo evaluate the accuracy and assess the effectiveness and worth of the proposed measure for linking news articles in Digital News Story Archive (DNSA), we adopted both, system-centric and user-centric (human judgment) evaluation over different datasets of news articles.FindingsThe proposed similarity measure is evaluated using different sizes of datasets, and the results are compared by both user-centric technique, i.e., expert judgment and system-centric techniques, i.e., cosine similarity measure, extended Jaccard measure and common ratio measure for stories (CRMS). The comparison helps to get a broader impact and can be helpful for generalization of the measure for different categories of news articles. Multiple experiments have conducted the findings of which showed that the measure presented viable results for national and international news, while best results for linking sports news articles during preservation based on headings.Originality/valueThe DNSA preserves a huge number of news articles from multiple news sources and to link with a vast collection, which encourages to introduce an efficient linking mechanism with few terms to manipulate. The CRMS is modified to deal with the headings of news articles as a part of the digital news stories preservation framework and comprehensively analysed.

ACS Style

Muzammil Khan; Sarwar Shah Khan; Arshad Ahmad; Arif Ur Rahman. The role of news title for linking during preservation process in digital archives. Library Hi Tech 2020, ahead-of-p, 1 .

AMA Style

Muzammil Khan, Sarwar Shah Khan, Arshad Ahmad, Arif Ur Rahman. The role of news title for linking during preservation process in digital archives. Library Hi Tech. 2020; ahead-of-p (ahead-of-p):1.

Chicago/Turabian Style

Muzammil Khan; Sarwar Shah Khan; Arshad Ahmad; Arif Ur Rahman. 2020. "The role of news title for linking during preservation process in digital archives." Library Hi Tech ahead-of-p, no. ahead-of-p: 1.

Journal article
Published: 03 November 2020 in Sensors
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The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

ACS Style

Muhammad Dawood Khan; Zahid Ullah; Arshad Ahmad; Bashir Hayat; Ahmad Almogren; Kyong Hoon Kim; Muhammad Ilyas; Muhammad Ali. Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN. Sensors 2020, 20, 6267 .

AMA Style

Muhammad Dawood Khan, Zahid Ullah, Arshad Ahmad, Bashir Hayat, Ahmad Almogren, Kyong Hoon Kim, Muhammad Ilyas, Muhammad Ali. Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN. Sensors. 2020; 20 (21):6267.

Chicago/Turabian Style

Muhammad Dawood Khan; Zahid Ullah; Arshad Ahmad; Bashir Hayat; Ahmad Almogren; Kyong Hoon Kim; Muhammad Ilyas; Muhammad Ali. 2020. "Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN." Sensors 20, no. 21: 6267.

Review
Published: 02 November 2020 in Applied Sciences
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Context: The use of controlled vocabularies (CVs) aims to increase the quality of the specifications of the software requirements, by producing well-written documentation to reduce both ambiguities and complexity. Many studies suggest that defects introduced at the requirements engineering (RE) phase have a negative impact, significantly higher than defects in the later stages of the software development lifecycle. However, the knowledge we have about the impact of using CVs, in specific RE activities, is very scarce. Objective: To identify and classify the type of CVs, and the impact they have on the requirements engineering phase of software development. Method: A systematic mapping study, collecting empirical evidence that is published up to July 2019. Results: This work identified 2348 papers published pertinent to CVs and RE, but only 90 primary published papers were chosen as relevant. The process of data extraction revealed that 79 studies reported the use of ontologies, whereas the remaining 11 were focused on taxonomies. The activities of RE with greater empirical support were those of specification (29 studies) and elicitation (28 studies). Seventeen different impacts of the CVs on the RE activities were classified and ranked, being the two most cited: guidance and understanding (38%), and automation and tool support (22%). Conclusions: The evolution of the last 10 years in the number of published papers shows that interest in the use of CVs remains high. The research community has a broad representation, distributed across the five continents. Most of the research focuses on the application of ontologies and taxonomies, whereas the use of thesauri and folksonomies is less reported. The evidence demonstrates the usefulness of the CVs in all RE activities, especially during elicitation and specification, helping developers understand, facilitating the automation process and identifying defects, conflicts and ambiguities in the requirements. Collaboration in research between academic and industrial contexts is low and should be promoted.

ACS Style

Arshad Ahmad; José Justo; Chong Feng; Arif Khan. The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study. Applied Sciences 2020, 10, 7749 .

AMA Style

Arshad Ahmad, José Justo, Chong Feng, Arif Khan. The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study. Applied Sciences. 2020; 10 (21):7749.

Chicago/Turabian Style

Arshad Ahmad; José Justo; Chong Feng; Arif Khan. 2020. "The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study." Applied Sciences 10, no. 21: 7749.

Research article
Published: 09 October 2020 in Complexity
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The rapidly growing data in many areas, as well as in the biomedical domain, require the assistance of information extraction systems to acquire the much needed knowledge about specific entities such as proteins, drugs, or diseases practically within a short time. Annotated corpora serve the purpose of facilitating the process of building NLP systems. While colossal work has been done in this area for English language, other languages like Arabic seem to lack these resources, especially in the healthcare area. Therefore, in this work, we present a method to develop a silver standard medical corpus for the Arabic language with a dictionary as a minimal supervision tool. The corpus contains 49,856 sentences tagged with 13 entity types corresponding to a subset of UMLS (Unified Medical Language System) concept types. The evaluation of a subset of corpus showed the efficiency of the method used to annotate it with 90% accuracy.

ACS Style

Nada Boudjellal; Huaping Zhang; Asif Khan; Arshad Ahmad; Rashid Naseem; Lin Dai. A Silver Standard Biomedical Corpus for Arabic Language. Complexity 2020, 2020, 1 -7.

AMA Style

Nada Boudjellal, Huaping Zhang, Asif Khan, Arshad Ahmad, Rashid Naseem, Lin Dai. A Silver Standard Biomedical Corpus for Arabic Language. Complexity. 2020; 2020 ():1-7.

Chicago/Turabian Style

Nada Boudjellal; Huaping Zhang; Asif Khan; Arshad Ahmad; Rashid Naseem; Lin Dai. 2020. "A Silver Standard Biomedical Corpus for Arabic Language." Complexity 2020, no. : 1-7.

Research article
Published: 22 September 2020 in Security and Communication Networks
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In this paper, we proposed LCX-MAC (local coordination X-MAC) as an extension of X-MAC. X-MAC is an asynchronous duty cycle medium access control (MAC) protocol. X-MAC used one important technique of short preamble which is to allow sender nodes to quickly send their actual data when the corresponding receivers wake up. X-MAC node keeps sending short preamble to wake up its receiver node, which causes energy, increases transmission delay, and makes the channel busy since a lot of short preambles are discarded, as these days Internet of Things (IoT) healthcare with different sensor nodes for the healthcare is time-critical applications and needs a quick response. A possible improvement over X-MAC is that local information of each node will share with its neighbour node. This local information exchanged will cause much less overhead than in the nodes which are synchronized. To calculate the effect of this the local coordination on X-MAC in this paper, we built an analytical model of LCX-MAC that incorporates the local coordination in X-MAC. The analytical results show that LCX-MAC outperformed X-MAC and X-MAC/BEB in terms of throughput, delay, and energy.

ACS Style

Arshad Ahmad; Ayaz Ullah; Chong Feng; Muzammil Khan; Shahzad Ashraf; Muhammad Adnan; Shah Nazir; Habib Ullah Khan. Towards an Improved Energy Efficient and End-to-End Secure Protocol for IoT Healthcare Applications. Security and Communication Networks 2020, 2020, 1 -10.

AMA Style

Arshad Ahmad, Ayaz Ullah, Chong Feng, Muzammil Khan, Shahzad Ashraf, Muhammad Adnan, Shah Nazir, Habib Ullah Khan. Towards an Improved Energy Efficient and End-to-End Secure Protocol for IoT Healthcare Applications. Security and Communication Networks. 2020; 2020 ():1-10.

Chicago/Turabian Style

Arshad Ahmad; Ayaz Ullah; Chong Feng; Muzammil Khan; Shahzad Ashraf; Muhammad Adnan; Shah Nazir; Habib Ullah Khan. 2020. "Towards an Improved Energy Efficient and End-to-End Secure Protocol for IoT Healthcare Applications." Security and Communication Networks 2020, no. : 1-10.

Review
Published: 21 September 2020 in Sensors
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As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.

ACS Style

Adnan Tahir; Fei Chen; Habib Ullah Khan; Zhong Ming; Arshad Ahmad; Shah Nazir; Muhammad Shafiq. A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems. Sensors 2020, 20, 5392 .

AMA Style

Adnan Tahir, Fei Chen, Habib Ullah Khan, Zhong Ming, Arshad Ahmad, Shah Nazir, Muhammad Shafiq. A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems. Sensors. 2020; 20 (18):5392.

Chicago/Turabian Style

Adnan Tahir; Fei Chen; Habib Ullah Khan; Zhong Ming; Arshad Ahmad; Shah Nazir; Muhammad Shafiq. 2020. "A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems." Sensors 20, no. 18: 5392.

Journal article
Published: 07 September 2020 in IEEE Access
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The advances in underwater sensor communication has become imperative getting up-to-date information about underwater happenings, especially when world has already faced the calamity like Tsunami. The underwater environment possessed freak and unpredictable movements which becomes more harsh time to time. The sensor nodes deployed under such juncture are the main source of information which in fact, facing numerous challenges. These nodes are mainly energy-constrained and rely on limited battery source. Due to most intricated underwater routing architecture, the biggest detriment is the limited battery lifespan. Therefore, it is imperative to adopt the pragmatic and possible alternate to improve the life expectancy of these sensor nodes. The solution of such shortcomings and identifying the varieties of impingements impelled by forwarding node on battery lifespan during packet transmission course are meticulously explored by developing an Underwater Pragmatic Routing Approach through Packet Reverberation mechanism (UPRA-PR). It is a novel approach and never considered in past. Through Packet Reverberation technique, the use of energy has been confined and the desired outcomes are achieved in four phases. In the first phase, the eligibility criteria for both packet and nodes have been computed by setting the Node Depth Factor (Ndf). Second phase formulates the forwarding relay node mechanism and rummage out the path failure by complying a Data Rate criterion D0. The selection of the shrewd communication link is established in third phase by considering an Accepted Link Quality (ALQ) factor. The fourth phase where most prominent developments has been made regarding impingements effects on the battery lifespan left by the forwarding node after the packet transmission. The UPRA-PR performance metrics are assessed by staging extensive NS2 simulation with AquaSim 2.0 and compared to state-existing routing protocols i.e., DBR, H2DAB, GEDAR and FBR for Packet dissemination ratio, Path failure, Point-to-point delay estimation, System energy consumption, Network lifespan, Forwarding node impingement and Network throughput. The simulation results have ratified the UPRA-PR performance and justified the statements made in this respect.

ACS Style

Shahzad Ashraf; Mingsheng Gao; Zhengming Chen; Hamad Naeem; Arshad Ahmad; Tauqeer Ahmed. Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism. IEEE Access 2020, 8, 163091 -163114.

AMA Style

Shahzad Ashraf, Mingsheng Gao, Zhengming Chen, Hamad Naeem, Arshad Ahmad, Tauqeer Ahmed. Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism. IEEE Access. 2020; 8 (99):163091-163114.

Chicago/Turabian Style

Shahzad Ashraf; Mingsheng Gao; Zhengming Chen; Hamad Naeem; Arshad Ahmad; Tauqeer Ahmed. 2020. "Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism." IEEE Access 8, no. 99: 163091-163114.

Research article
Published: 01 September 2020 in Scientific Programming
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Politics is one of the hottest and most commonly mentioned and viewed topics on social media networks nowadays. Microblogging platforms like Twitter and Weibo are widely used by many politicians who have a huge number of followers and supporters on those platforms. It is essential to study the supporters’ network of political leaders because it can help in decision making when predicting their political futures. This study focuses on the supporters’ network of three famous political leaders of Pakistan, namely, Imran Khan (IK), Maryam Nawaz Sharif (MNS), and Bilawal Bhutto Zardari (BBZ). This is done using social network analysis and semantic analysis. The proposed method (1) detects and removes fake supporter(s), (2) mines communities in the politicians’ social network(s), (3) investigates the supporters’ reply network for conversations between supporters about each leader, and, finally, (4) analyses the retweet network for information diffusion of each political leader. Furthermore, sentiment analysis of the supporters of politicians is done using machine learning techniques, which ultimately predicted and revealed the strongest supporter network(s) among the three political leaders. Analysis of this data reveals that as of October 2017 (1) IK was the most renowned of the three politicians and had the strongest supporter’s community while using Twitter in a very controlled manner, (2) BBZ had the weakest supporters’ network on Twitter, and (3) the supporters of the political leaders in Pakistan are flexible on Twitter, communicating with each other, and that any group of supporters has a low level of isolation.

ACS Style

Asif Khan; Huaping Zhang; Jianyun Shang; Nada Boudjellal; Arshad Ahmad; Asmat Ali; Lin Dai. Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis. Scientific Programming 2020, 2020, 1 -17.

AMA Style

Asif Khan, Huaping Zhang, Jianyun Shang, Nada Boudjellal, Arshad Ahmad, Asmat Ali, Lin Dai. Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis. Scientific Programming. 2020; 2020 ():1-17.

Chicago/Turabian Style

Asif Khan; Huaping Zhang; Jianyun Shang; Nada Boudjellal; Arshad Ahmad; Asmat Ali; Lin Dai. 2020. "Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis." Scientific Programming 2020, no. : 1-17.

Research article
Published: 03 August 2020 in Journal of Information Science
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To retrieve a specific news article from a vast archive containing multilingual news articles against a user query or based on similarity among news articles is a challenging task. The task becomes even further complicated when the archive contains articles from a low resourced and morphologically complex language like Urdu, along with English new articles. The article proposes a content-based (lexical) similarity measure, that is, Common Ratio Measure for Dual Language (CRMDL), for linking digital news articles published in various online news sources. The similarity measure links Urdu-to-English news articles during the preservation process using an Urdu-to-English lexicon. A literature review showed that an Urdu-to-English lexicon did not exist, and therefore, the first task was to build a lexicon from multiple sources. The proposed similarity measure, that is, CRMDL, is evaluated rigorously on different data sets, of varying sizes, to assess the effectiveness. The experimental results show that the proposed measure is feasible and effective for similarity computation between Urdu and English news articles, which can obtain, on average, 50% precision and 67% recall. The performance can be improved sufficiently by managing the limitations summarised in the study.

ACS Style

Muzammil Khan; Arif Ur Rahman; Arshad Ahmad; Sarwar Shah Khan. A content-based technique for linking dual language news articles in an archive. Journal of Information Science 2020, 1 .

AMA Style

Muzammil Khan, Arif Ur Rahman, Arshad Ahmad, Sarwar Shah Khan. A content-based technique for linking dual language news articles in an archive. Journal of Information Science. 2020; ():1.

Chicago/Turabian Style

Muzammil Khan; Arif Ur Rahman; Arshad Ahmad; Sarwar Shah Khan. 2020. "A content-based technique for linking dual language news articles in an archive." Journal of Information Science , no. : 1.

Review
Published: 15 July 2020 in Security and Communication Networks
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Context. The improvements made in the last couple of decades in the requirements engineering (RE) processes and methods have witnessed a rapid rise in effectively using diverse machine learning (ML) techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow (SO) for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing (NLP) techniques. Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. Objective. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. Method. This paper reports a systematic literature review (SLR) collecting empirical evidence published up to May 2020. Results. This SLR study found 2,484 published papers related to RE and SO. The data extraction process of the SLR showed that (1) Latent Dirichlet Allocation (LDA) topic modeling is among the widely used ML algorithm in the selected studies and (2) precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms. Conclusion. Our SLR study revealed that while ML algorithms have phenomenal capabilities of identifying the software requirements on SO, they still are confronted with various open problems/issues that will eventually limit their practical applications and performances. Our SLR study calls for the need of close collaboration venture between the RE and ML communities/researchers to handle the open issues confronted in the development of some real world machine learning-based quality systems.

ACS Style

Arshad Ahmad; Chong Feng; Muzammil Khan; Asif Khan; Ayaz Ullah; Shah Nazir; Adnan Tahir. A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow. Security and Communication Networks 2020, 2020, 1 -19.

AMA Style

Arshad Ahmad, Chong Feng, Muzammil Khan, Asif Khan, Ayaz Ullah, Shah Nazir, Adnan Tahir. A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow. Security and Communication Networks. 2020; 2020 ():1-19.

Chicago/Turabian Style

Arshad Ahmad; Chong Feng; Muzammil Khan; Asif Khan; Ayaz Ullah; Shah Nazir; Adnan Tahir. 2020. "A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow." Security and Communication Networks 2020, no. : 1-19.

Research article
Published: 14 July 2020 in Scientific Programming
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With the advancement in ICT, web search engines have become a preferred source to find health-related information published over the Internet. Google alone receives more than one billion health-related queries on a daily basis. However, in order to provide the results most relevant to the user, WSEs maintain the users’ profiles. These profiles may contain private and sensitive information such as the user’s health condition, disease status, and others. Health-related queries contain privacy-sensitive information that may infringe user’s privacy, as the identity of a user is exposed and may be misused by the WSE and third parties. This raises serious concerns since the identity of a user is exposed and may be misused by third parties. One well-known solution to preserve privacy involves issuing the queries via peer-to-peer private information retrieval protocol, such as useless user profile (UUP), thereby hiding the user’s identity from the WSE. This paper investigates the level of protection offered by UUP. For this purpose, we present QuPiD (query profile distance) attack: a machine learning-based attack that evaluates the effectiveness of UUP in privacy protection. QuPiD attack determines the distance between the user’s profile (web search history) and upcoming query using our proposed novel feature vector. The experiments were conducted using ten classification algorithms belonging to the tree-based, rule-based, lazy learner, metaheuristic, and Bayesian families for the sake of comparison. Furthermore, two subsets of an America Online dataset (noisy and clean datasets) were used for experimentation. The results show that the proposed QuPiD attack associates more than 70% queries to the correct user with a precision of over 72% for the clean dataset, while for the noisy dataset, the proposed QuPiD attack associates more than 40% queries to the correct user with 70% precision.

ACS Style

Rafiullah Khan; Arshad Ahmad; Alhuseen Omar Alsayed; Muhammad Binsawad; Muhammad Arshad Islam; Mohib Ullah. QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search. Scientific Programming 2020, 2020, 1 -11.

AMA Style

Rafiullah Khan, Arshad Ahmad, Alhuseen Omar Alsayed, Muhammad Binsawad, Muhammad Arshad Islam, Mohib Ullah. QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search. Scientific Programming. 2020; 2020 ():1-11.

Chicago/Turabian Style

Rafiullah Khan; Arshad Ahmad; Alhuseen Omar Alsayed; Muhammad Binsawad; Muhammad Arshad Islam; Mohib Ullah. 2020. "QuPiD Attack: Machine Learning-Based Privacy Quantification Mechanism for PIR Protocols in Health-Related Web Search." Scientific Programming 2020, no. : 1-11.