The introductory programming course (IPC) holds a special significance in computing disciplines a... more The introductory programming course (IPC) holds a special significance in computing disciplines as this course serves as a prerequisite for studying the higher level courses. Students generally face difficulties during their initial stages of learning how to program. Continuous efforts are being made to examine this course for identifying potential improvements. This article presents the review of the state-of-the-art research exploring various components of IPC by examining sixty-six articles published between 2014 and 2020 in well-reputed research venues. The results reveal that several useful methods have been proposed to support teaching and learning in IPC. Moreover, the research in IPC presented useful ways to conduct assessments, and also demonstrated different techniques to examine improvements in the IPC contents. In addition, a variety of tools are evaluated to support the related course processes. Apart from the aforementioned facets, this research explores other interest...
Sign languages are used by the deaf and mute community of the world. These are gesture based lang... more Sign languages are used by the deaf and mute community of the world. These are gesture based languages where the subjects use hands and facial expressions to perform different gestures. There are hundreds of different sign languages in the world. Furthermore, like natural languages, there exist different dialects for many sign languages. In order to facilitate the deaf community several different repositories of video gestures are available for many sign languages of the world. These video based repositories do not support the development of an automated language translation systems. This research aims to investigate the idea of engaging the deaf community for the development and validation of a parallel corpus for a sign language and its dialects. As a principal contribution, this research presents a framework for building a parallel corpus for sign languages by harnessing the powers of crowdsourcing with editorial manager, thus it engages a diversified set of stakeholders for building and validating a repository in a quality controlled manner. It further presents processes to develop a word-level parallel corpus for different dialects of a sign language; and a process to develop sentence-level translation corpus comprising of source and translated sentences. The proposed framework has been successfully implemented and involved different stakeholders to build corpus. As a result, a word-level parallel corpus comprising of the gestures of almost 700 words of Pakistan Sign Language (PSL) has been developed. While, a sentence-level translation corpus comprising of more than 8000 sentences for different tenses has also been developed for PSL. This sentence-level corpus can be used in developing and evaluating machine translation models for natural to sign language translation and vice-versa. While the machine-readable word level parallel corpus will help in generating avatar based videos for the translated sentences in different dialects of a sign language.
There has been significant development in the facial recognition technology during past few decad... more There has been significant development in the facial recognition technology during past few decades. This technology has been widely used by different organizations and governments for defense, security, and surveillance projects. Furthermore, it has now been incorporated into our daily usages, such as consumer applications, personal data protection, or cyber-security, particularly while using smartphones. Most of these systems work very efficient, however, there are some challenges related to the accuracy of results of facial recognition systems when tested on images of people with dark skin. As a matter of fact, various studies demonstrate higher accuracy when tested on data set with white skin personnel, while exhibit a much lesser accuracy when the same algorithms are tested on dataset of people with dark skin. This article highlights the variation in accuracy of existing facial recognition algorithms when applied to dark-skinned people. Furthermore, as a principal contribution it presents a hybrid algorithm based on Gaussian and Explicit rule model that improves the accuracy for face-detection for dark skinned people. Thorough experimental evaluation has been conducted with a data set of black faces by first identifying skin and non-skin regions and then applying skin segmentation. The results have been compared with existing face detection algorithms with a clear improvement in the accuracy of 89% for dark skin.
Effectiveness of Literacy Numeracy Drive (LND): A Students' Perspective
2019 International Conference on Innovative Computing (ICIC)
This paper discussed the effectiveness of English language software Literacy & Numeracy D... more This paper discussed the effectiveness of English language software Literacy & Numeracy Drive (LND) used in the public sector primary schools of Punjab province, Pakistan. A mobile based application named as "Literacy & Numeracy Drive (LND)" is currently in use to teach students of Grade 03 on a tablet for learning English, Urdu and Mathematics. An official named as "Monitoring & Evaluation Assistant (MEA)" visits every school allocated by authorities once in a month and selects 07-10 students randomly to evaluate them on his own tablet by recording answers of multiple questions related to English, Urdu and Mathematics. After the evaluation, MEA uploads the results of respective school on official portal. A quantitative research was adopted to conduct this study in which 300 participants from different schools of district Sheikhupura were selected to measure the effectiveness of LND. The results reveal that LND application was not found effective due to the issues in terms of non-availability of technology at home, language, content, assessment, usability and design. Further, students recommended game based learning consisting of interactive interface, phonics, animations, relevant content and assessment.
Learning to program involves acquisition of various skills including problem solving, fundamental... more Learning to program involves acquisition of various skills including problem solving, fundamental design techniques as well as critical thinking. Generally, most of the novice programmers struggle to develop all these important skill. The research community has addressed the problem in many different ways while involving improvisations in curriculum, pedagogical methods, cognitive aspects, supporting tools, and in designing assessments. This research aims to analyze and synthesize the existing literature in the aforementioned areas. Research articles pertaining to the area of Introductory Programming Courses (IPC) have been found using appropriate search queries, while nearly 60 research articles, published in last ten years, have been carefully selected by employing a systematic filtering process. The scope of this work only covers the research conducted for IPC in higher education. Main findings of this study show that ''solution proposal'' and ''evaluation research'' have been reported as two main research types adopted by these studies. Moreover, pedagogy, language choice and students' performance analysis are the most frequently addressed aspects of IPC; whereas, curriculum contents, assessment design, and teaching/learning through tools have appeared as less addressed aspects of IPC. Furthermore, a taxonomy of IPC has been presented based on the studied literature. Lastly, general considerations and future research directions have been presented for the practitioners and researchers in this area.
In the current era, automatic surveillance has become an active research problem due to its vast ... more In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.
Intention mining is a promising research area of data mining that aims to determine end-users’ in... more Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and...
Cloud computing has become a very important computing model to process data and execute computati... more Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of ...
Students of initial level programming courses generally face difficulties while learning the prog... more Students of initial level programming courses generally face difficulties while learning the programming concepts. The learning analytics studies, in these courses, are mostly anecdotal on the aspect of assessment as less or no attention is given to assess learning at various cognitive levels of specific concepts. Furthermore, the existing work reflects deficiencies in examining the effect of learners’ cognitive performance on subsequent stages of the course. This gap needs to be addressed by introducing more granular and methodical approaches of cognitive analysis for sustaining the programming courses effectively in computer science and associated disciplines. In this article, a framework-based approach is proposed for cognitive learning analytics on the concepts taught in initial level programming courses. The framework serves as a platform that provides structure to the concept data using the technique of concept mapping and examines learners’ cognitive propagation on related co...
International Journal of Advanced Computer Science and Applications
This paper aims to look at teachers, government officials, and students for Literacy & Numeracy D... more This paper aims to look at teachers, government officials, and students for Literacy & Numeracy Drive (LND), a smartphone app for students in Punjab province, Pakistan, to teach languages and math. Furthermore, to recognize LND usability and design problems while its use for grade three in schools. As the usability and design issues of LND were not discussed since the launch of this application. The methodology for this study is the questionnaire for teachers and semistructured interviews for government officials of District Sheikhupura and students. The result shows that LND has various usability and design problems in its current form, i.e., buttons, icons, color schemes, sluggish performance, and fonts. Besides, teachers, government officials, and students suggested that game-based learning consists of an interactive interface, phonics, key animations to be created and adopted. Highly engaging and appealing delivery of the curriculum and improvements in the appraisal will improve the participation of students and deliver better outcomes.
International Journal of Distributed Sensor Networks
In the past few decades, the whole world has been badly affected by terrorism and other law-and-o... more In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 50...
The recruitment, engagement, and training of the employees are among the core and critical roles ... more The recruitment, engagement, and training of the employees are among the core and critical roles for running an organization successfully. Gamification has been proven to be a promising tool that offers many innovative solutions for various domains. This study presents a systematic literature review (SLR) of research studies involving gamification for recruitment, engagement, and training of employees. The SLR has been synthesized by reviewing carefully selected research studies published between 2014 and 2019 in well-reputed venues. Apart from reviewing the state-of-the-art studies on gamified solutions for recruiting and job training problems, a taxonomy of gaming elements based on the context of use in different environments has also been proposed. Similarly, the gamification model to develop gamified solutions to problems has also been presented. Lastly, the research gaps in terms of open issues have been presented to provide future directions for the researchers in the area of gamified solutions for recruitment and job training. INDEX TERMS Gamification, recruitment, employee engagement, employee training, and gaming elements.
Fuzzy Expert System (FES) with application to earthquake prediction has been presented to reprodu... more Fuzzy Expert System (FES) with application to earthquake prediction has been presented to reproduce the performance of a human expert in earthquake prediction using expert systems. This research aims to predict future earthquakes having a magnitude 5.5 or greater. Previous earthquake data from 2000 to 2019 have been collected for this purpose. Since the earthquake data for the specified region have been reported on different magnitude scales, suitable relationships were determined to obtain uniform data. The uniform data have been used to calculate seismicity indicators according to the guidelines provided by Gutenberg-Richter’s scale for quantitative determination of earthquake features. The relationships among these seismic indicators have been used by the human expert to set the rule base of Fuzzy expert system. These rules have been mathematically validated and tested on instrumentally recorded earthquake data. The results obtained from the proposed FES presented 47 % accuracy i...
Peptides, short-chained amino acids, have shown great potentials toward the investigation and evo... more Peptides, short-chained amino acids, have shown great potentials toward the investigation and evolution of novel medications for treatment or therapy. The wet-lab based discovery of potential therapeutic peptides and eventually drug development is a hard and time-consuming process. The computational prediction using machine learning (ML) methods can expedite and facilitate the discovery process of potential prospects with therapeutic effects. ML approaches have been practiced favorably and extensively within the area of proteins, DNA, and RNA to discover the hidden features and functional activities, moreover, recently been utilized for functional discovery of peptides for various therapeutics. In this paper, a systematic literature review (SLR) has been presented to recognize the data-sources, ML classifiers, and encoding schemes being utilized in the state-of-the-art computational models to predict therapeutic peptides. To conduct the SLR, fourty-one research articles have been selected carefully based on well-defined selection criteria. To the best of our knowledge, there is no such SLR available that provides a comprehensive review in this domain. In this article, we have proposed a taxonomy based on identified feature encodings, which may offer relational understandings to researchers. Similarly, the framework model for the computational prediction of the therapeutic peptides has been introduced to characterize the best practices and levels involved in the development of peptide prediction models. Lastly, common issues and challenges have been discussed to facilitate the researchers with encouraging future directions in the field of computational prediction of therapeutic peptides.
Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based c... more Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.
The recruitment, engagement, and training of the employees are among the core and critical roles ... more The recruitment, engagement, and training of the employees are among the core and critical roles for running an organization successfully. Gamification has been proven to be a promising tool that offers many innovative solutions for various domains. This study presents a systematic literature review (SLR) of research studies involving gamification for recruitment, engagement, and training of employees. The SLR has been synthesized by reviewing carefully selected research studies published between 2014 and 2019 in well-reputed venues. Apart from reviewing the state-of-the-art studies on gamified solutions for recruiting and job training problems, a taxonomy of gaming elements based on the context of use in different environments has also been proposed. Similarly, the gamification model to develop gamified solutions to problems has also been presented. Lastly, the research gaps in terms of open issues have been presented to provide future directions for the researchers in the area of gamified solutions for recruitment and job training. INDEX TERMS Gamification, recruitment, employee engagement, employee training, and gaming elements.
Automatic news repository collection systems involve a news crawler that extracts news from diffe... more Automatic news repository collection systems involve a news crawler that extracts news from different news portals, subsequently, these news need to be processed to figure out the category of a news article e.g. sports, politics, showbiz etc. In this process there are two main challenges first one is to place a news article under the right category of news, while the second one is to detect a duplicate news, i.e. when the news are being extracted from multiple sources, it is highly probable to get the same news from many different portals, resulting into duplicate news; failing to which may result into inconsistent statistics obtained after pre-processing the news text. This problem becomes more pertinent when we deal with human loss news involving crime, accident etc. related news articles. As the system may count the same news many times resulting into misleading statistics. In order to address these problems, this research presents the following contributions. Firstly, a news corpus comprising of human loss news of different categories has been developed by gathering data from different sources of well-known and authentic news websites. The corpus also includes a number of duplicate news. Secondly, a comparison of different classification approaches has been conducted to empirically find out the best suitable text classifier for the categorization of different sub-categories of human loss news. Lastly, methods have been proposed and compared to detect duplicate news from the corpus by involving different pre-processing techniques and widely used similarity measures, cosine similarity, and Jaccard's coefficient. The results show that conventional text classifiers are still relevant and perform well in text classification tasks as MNB has given 89.5% accurate results. While, Jaccard coefficient exhibits much better results than Cosine similarity for duplicate news detection with different pre-processing variations with an average accuracy of 83.16%.
This paper aims to look at teachers, government officials, and students for Literacy & Numeracy D... more This paper aims to look at teachers, government officials, and students for Literacy & Numeracy Drive (LND), a smartphone app for students in Punjab province, Pakistan, to teach languages and math. Furthermore, to recognize LND usability and design problems while its use for grade three in schools. As the usability and design issues of LND were not discussed since the launch of this application. The methodology for this study is the questionnaire for teachers and semi-structured interviews for government officials of District Sheikhupura and students. The result shows that LND has various usability and design problems in its current form, i.e., buttons, icons, color schemes, sluggish performance, and fonts. Besides, teachers, government officials, and students suggested that game-based learning consists of an interactive interface, phonics, key animations to be created and adopted. Highly engaging and appealing delivery of the curriculum and improvements in the appraisal will improv...
The introductory programming course (IPC) holds a special significance in computing disciplines a... more The introductory programming course (IPC) holds a special significance in computing disciplines as this course serves as a prerequisite for studying the higher level courses. Students generally face difficulties during their initial stages of learning how to program. Continuous efforts are being made to examine this course for identifying potential improvements. This article presents the review of the state-of-the-art research exploring various components of IPC by examining sixty-six articles published between 2014 and 2020 in well-reputed research venues. The results reveal that several useful methods have been proposed to support teaching and learning in IPC. Moreover, the research in IPC presented useful ways to conduct assessments, and also demonstrated different techniques to examine improvements in the IPC contents. In addition, a variety of tools are evaluated to support the related course processes. Apart from the aforementioned facets, this research explores other interest...
Sign languages are used by the deaf and mute community of the world. These are gesture based lang... more Sign languages are used by the deaf and mute community of the world. These are gesture based languages where the subjects use hands and facial expressions to perform different gestures. There are hundreds of different sign languages in the world. Furthermore, like natural languages, there exist different dialects for many sign languages. In order to facilitate the deaf community several different repositories of video gestures are available for many sign languages of the world. These video based repositories do not support the development of an automated language translation systems. This research aims to investigate the idea of engaging the deaf community for the development and validation of a parallel corpus for a sign language and its dialects. As a principal contribution, this research presents a framework for building a parallel corpus for sign languages by harnessing the powers of crowdsourcing with editorial manager, thus it engages a diversified set of stakeholders for building and validating a repository in a quality controlled manner. It further presents processes to develop a word-level parallel corpus for different dialects of a sign language; and a process to develop sentence-level translation corpus comprising of source and translated sentences. The proposed framework has been successfully implemented and involved different stakeholders to build corpus. As a result, a word-level parallel corpus comprising of the gestures of almost 700 words of Pakistan Sign Language (PSL) has been developed. While, a sentence-level translation corpus comprising of more than 8000 sentences for different tenses has also been developed for PSL. This sentence-level corpus can be used in developing and evaluating machine translation models for natural to sign language translation and vice-versa. While the machine-readable word level parallel corpus will help in generating avatar based videos for the translated sentences in different dialects of a sign language.
There has been significant development in the facial recognition technology during past few decad... more There has been significant development in the facial recognition technology during past few decades. This technology has been widely used by different organizations and governments for defense, security, and surveillance projects. Furthermore, it has now been incorporated into our daily usages, such as consumer applications, personal data protection, or cyber-security, particularly while using smartphones. Most of these systems work very efficient, however, there are some challenges related to the accuracy of results of facial recognition systems when tested on images of people with dark skin. As a matter of fact, various studies demonstrate higher accuracy when tested on data set with white skin personnel, while exhibit a much lesser accuracy when the same algorithms are tested on dataset of people with dark skin. This article highlights the variation in accuracy of existing facial recognition algorithms when applied to dark-skinned people. Furthermore, as a principal contribution it presents a hybrid algorithm based on Gaussian and Explicit rule model that improves the accuracy for face-detection for dark skinned people. Thorough experimental evaluation has been conducted with a data set of black faces by first identifying skin and non-skin regions and then applying skin segmentation. The results have been compared with existing face detection algorithms with a clear improvement in the accuracy of 89% for dark skin.
Effectiveness of Literacy Numeracy Drive (LND): A Students' Perspective
2019 International Conference on Innovative Computing (ICIC)
This paper discussed the effectiveness of English language software Literacy & Numeracy D... more This paper discussed the effectiveness of English language software Literacy & Numeracy Drive (LND) used in the public sector primary schools of Punjab province, Pakistan. A mobile based application named as "Literacy & Numeracy Drive (LND)" is currently in use to teach students of Grade 03 on a tablet for learning English, Urdu and Mathematics. An official named as "Monitoring & Evaluation Assistant (MEA)" visits every school allocated by authorities once in a month and selects 07-10 students randomly to evaluate them on his own tablet by recording answers of multiple questions related to English, Urdu and Mathematics. After the evaluation, MEA uploads the results of respective school on official portal. A quantitative research was adopted to conduct this study in which 300 participants from different schools of district Sheikhupura were selected to measure the effectiveness of LND. The results reveal that LND application was not found effective due to the issues in terms of non-availability of technology at home, language, content, assessment, usability and design. Further, students recommended game based learning consisting of interactive interface, phonics, animations, relevant content and assessment.
Learning to program involves acquisition of various skills including problem solving, fundamental... more Learning to program involves acquisition of various skills including problem solving, fundamental design techniques as well as critical thinking. Generally, most of the novice programmers struggle to develop all these important skill. The research community has addressed the problem in many different ways while involving improvisations in curriculum, pedagogical methods, cognitive aspects, supporting tools, and in designing assessments. This research aims to analyze and synthesize the existing literature in the aforementioned areas. Research articles pertaining to the area of Introductory Programming Courses (IPC) have been found using appropriate search queries, while nearly 60 research articles, published in last ten years, have been carefully selected by employing a systematic filtering process. The scope of this work only covers the research conducted for IPC in higher education. Main findings of this study show that ''solution proposal'' and ''evaluation research'' have been reported as two main research types adopted by these studies. Moreover, pedagogy, language choice and students' performance analysis are the most frequently addressed aspects of IPC; whereas, curriculum contents, assessment design, and teaching/learning through tools have appeared as less addressed aspects of IPC. Furthermore, a taxonomy of IPC has been presented based on the studied literature. Lastly, general considerations and future research directions have been presented for the practitioners and researchers in this area.
In the current era, automatic surveillance has become an active research problem due to its vast ... more In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.
Intention mining is a promising research area of data mining that aims to determine end-users’ in... more Intention mining is a promising research area of data mining that aims to determine end-users’ intentions from their past activities stored in the logs, which note users’ interaction with the system. Search engines are a major source to infer users’ past searching activities to predict their intention, facilitating the vendors and manufacturers to present their products to the user in a promising manner. This area has been consistently getting pertinence with an increasing trend for online purchasing. Noticeable research work has been accomplished in this area for the last two decades. There is no such systematic literature review available that provides a comprehensive review in intension mining domain to the best of our knowledge. This article presents a systematic literature review based on 109 high-quality research papers selected after rigorous screening. The analysis reveals that there exist eight prominent categories of intention. Furthermore, a taxonomy of the approaches and...
Cloud computing has become a very important computing model to process data and execute computati... more Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of ...
Students of initial level programming courses generally face difficulties while learning the prog... more Students of initial level programming courses generally face difficulties while learning the programming concepts. The learning analytics studies, in these courses, are mostly anecdotal on the aspect of assessment as less or no attention is given to assess learning at various cognitive levels of specific concepts. Furthermore, the existing work reflects deficiencies in examining the effect of learners’ cognitive performance on subsequent stages of the course. This gap needs to be addressed by introducing more granular and methodical approaches of cognitive analysis for sustaining the programming courses effectively in computer science and associated disciplines. In this article, a framework-based approach is proposed for cognitive learning analytics on the concepts taught in initial level programming courses. The framework serves as a platform that provides structure to the concept data using the technique of concept mapping and examines learners’ cognitive propagation on related co...
International Journal of Advanced Computer Science and Applications
This paper aims to look at teachers, government officials, and students for Literacy & Numeracy D... more This paper aims to look at teachers, government officials, and students for Literacy & Numeracy Drive (LND), a smartphone app for students in Punjab province, Pakistan, to teach languages and math. Furthermore, to recognize LND usability and design problems while its use for grade three in schools. As the usability and design issues of LND were not discussed since the launch of this application. The methodology for this study is the questionnaire for teachers and semistructured interviews for government officials of District Sheikhupura and students. The result shows that LND has various usability and design problems in its current form, i.e., buttons, icons, color schemes, sluggish performance, and fonts. Besides, teachers, government officials, and students suggested that game-based learning consists of an interactive interface, phonics, key animations to be created and adopted. Highly engaging and appealing delivery of the curriculum and improvements in the appraisal will improve the participation of students and deliver better outcomes.
International Journal of Distributed Sensor Networks
In the past few decades, the whole world has been badly affected by terrorism and other law-and-o... more In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 50...
The recruitment, engagement, and training of the employees are among the core and critical roles ... more The recruitment, engagement, and training of the employees are among the core and critical roles for running an organization successfully. Gamification has been proven to be a promising tool that offers many innovative solutions for various domains. This study presents a systematic literature review (SLR) of research studies involving gamification for recruitment, engagement, and training of employees. The SLR has been synthesized by reviewing carefully selected research studies published between 2014 and 2019 in well-reputed venues. Apart from reviewing the state-of-the-art studies on gamified solutions for recruiting and job training problems, a taxonomy of gaming elements based on the context of use in different environments has also been proposed. Similarly, the gamification model to develop gamified solutions to problems has also been presented. Lastly, the research gaps in terms of open issues have been presented to provide future directions for the researchers in the area of gamified solutions for recruitment and job training. INDEX TERMS Gamification, recruitment, employee engagement, employee training, and gaming elements.
Fuzzy Expert System (FES) with application to earthquake prediction has been presented to reprodu... more Fuzzy Expert System (FES) with application to earthquake prediction has been presented to reproduce the performance of a human expert in earthquake prediction using expert systems. This research aims to predict future earthquakes having a magnitude 5.5 or greater. Previous earthquake data from 2000 to 2019 have been collected for this purpose. Since the earthquake data for the specified region have been reported on different magnitude scales, suitable relationships were determined to obtain uniform data. The uniform data have been used to calculate seismicity indicators according to the guidelines provided by Gutenberg-Richter’s scale for quantitative determination of earthquake features. The relationships among these seismic indicators have been used by the human expert to set the rule base of Fuzzy expert system. These rules have been mathematically validated and tested on instrumentally recorded earthquake data. The results obtained from the proposed FES presented 47 % accuracy i...
Peptides, short-chained amino acids, have shown great potentials toward the investigation and evo... more Peptides, short-chained amino acids, have shown great potentials toward the investigation and evolution of novel medications for treatment or therapy. The wet-lab based discovery of potential therapeutic peptides and eventually drug development is a hard and time-consuming process. The computational prediction using machine learning (ML) methods can expedite and facilitate the discovery process of potential prospects with therapeutic effects. ML approaches have been practiced favorably and extensively within the area of proteins, DNA, and RNA to discover the hidden features and functional activities, moreover, recently been utilized for functional discovery of peptides for various therapeutics. In this paper, a systematic literature review (SLR) has been presented to recognize the data-sources, ML classifiers, and encoding schemes being utilized in the state-of-the-art computational models to predict therapeutic peptides. To conduct the SLR, fourty-one research articles have been selected carefully based on well-defined selection criteria. To the best of our knowledge, there is no such SLR available that provides a comprehensive review in this domain. In this article, we have proposed a taxonomy based on identified feature encodings, which may offer relational understandings to researchers. Similarly, the framework model for the computational prediction of the therapeutic peptides has been introduced to characterize the best practices and levels involved in the development of peptide prediction models. Lastly, common issues and challenges have been discussed to facilitate the researchers with encouraging future directions in the field of computational prediction of therapeutic peptides.
Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based c... more Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.
The recruitment, engagement, and training of the employees are among the core and critical roles ... more The recruitment, engagement, and training of the employees are among the core and critical roles for running an organization successfully. Gamification has been proven to be a promising tool that offers many innovative solutions for various domains. This study presents a systematic literature review (SLR) of research studies involving gamification for recruitment, engagement, and training of employees. The SLR has been synthesized by reviewing carefully selected research studies published between 2014 and 2019 in well-reputed venues. Apart from reviewing the state-of-the-art studies on gamified solutions for recruiting and job training problems, a taxonomy of gaming elements based on the context of use in different environments has also been proposed. Similarly, the gamification model to develop gamified solutions to problems has also been presented. Lastly, the research gaps in terms of open issues have been presented to provide future directions for the researchers in the area of gamified solutions for recruitment and job training. INDEX TERMS Gamification, recruitment, employee engagement, employee training, and gaming elements.
Automatic news repository collection systems involve a news crawler that extracts news from diffe... more Automatic news repository collection systems involve a news crawler that extracts news from different news portals, subsequently, these news need to be processed to figure out the category of a news article e.g. sports, politics, showbiz etc. In this process there are two main challenges first one is to place a news article under the right category of news, while the second one is to detect a duplicate news, i.e. when the news are being extracted from multiple sources, it is highly probable to get the same news from many different portals, resulting into duplicate news; failing to which may result into inconsistent statistics obtained after pre-processing the news text. This problem becomes more pertinent when we deal with human loss news involving crime, accident etc. related news articles. As the system may count the same news many times resulting into misleading statistics. In order to address these problems, this research presents the following contributions. Firstly, a news corpus comprising of human loss news of different categories has been developed by gathering data from different sources of well-known and authentic news websites. The corpus also includes a number of duplicate news. Secondly, a comparison of different classification approaches has been conducted to empirically find out the best suitable text classifier for the categorization of different sub-categories of human loss news. Lastly, methods have been proposed and compared to detect duplicate news from the corpus by involving different pre-processing techniques and widely used similarity measures, cosine similarity, and Jaccard's coefficient. The results show that conventional text classifiers are still relevant and perform well in text classification tasks as MNB has given 89.5% accurate results. While, Jaccard coefficient exhibits much better results than Cosine similarity for duplicate news detection with different pre-processing variations with an average accuracy of 83.16%.
This paper aims to look at teachers, government officials, and students for Literacy & Numeracy D... more This paper aims to look at teachers, government officials, and students for Literacy & Numeracy Drive (LND), a smartphone app for students in Punjab province, Pakistan, to teach languages and math. Furthermore, to recognize LND usability and design problems while its use for grade three in schools. As the usability and design issues of LND were not discussed since the launch of this application. The methodology for this study is the questionnaire for teachers and semi-structured interviews for government officials of District Sheikhupura and students. The result shows that LND has various usability and design problems in its current form, i.e., buttons, icons, color schemes, sluggish performance, and fonts. Besides, teachers, government officials, and students suggested that game-based learning consists of an interactive interface, phonics, key animations to be created and adopted. Highly engaging and appealing delivery of the curriculum and improvements in the appraisal will improv...
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Papers by DR. ADNAN ABID