Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972959
Shuja Ali, Muhammad Hanzla, A. Rafique
Traffic monitoring plays a vital role in the current world. Previously, stationary data collectors such as video cameras and induction loops were employed for this task. However, the availability of unmanned aerial vehicles (UAV) has opened up new horizons for this task and numerous research projects are being conducted in this field. But object detection and tracking become a challenging task in the case of aerial images due to the presence of high density of objects, challenging view angles, different illumination changes, and varying altitudes of the drone. In this paper, we propose a method for detecting vehicles and also tracking them through the use of cascade classifier and centroid tracking. We have also incorporated georeferencing and coregistration of acquired images and then proceeded on to extract lanes. After segmenting out the region of interest, we proceeded with the detection and tracking tasks.
{"title":"Vehicle Detection and Tracking from UAV Imagery via Cascade Classifier","authors":"Shuja Ali, Muhammad Hanzla, A. Rafique","doi":"10.1109/INMIC56986.2022.9972959","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972959","url":null,"abstract":"Traffic monitoring plays a vital role in the current world. Previously, stationary data collectors such as video cameras and induction loops were employed for this task. However, the availability of unmanned aerial vehicles (UAV) has opened up new horizons for this task and numerous research projects are being conducted in this field. But object detection and tracking become a challenging task in the case of aerial images due to the presence of high density of objects, challenging view angles, different illumination changes, and varying altitudes of the drone. In this paper, we propose a method for detecting vehicles and also tracking them through the use of cascade classifier and centroid tracking. We have also incorporated georeferencing and coregistration of acquired images and then proceeded on to extract lanes. After segmenting out the region of interest, we proceeded with the detection and tracking tasks.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125334778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972908
N. Perwaiz, M. Shahzad, M. Fraz
Person re-identification (Re-ID) is a process to re-identify a person if he has been already seen by a camera network. Since start the convolutional neural networks (CNNs) are dominantly being used to solve the person Re-ID problem. The default limitation of CNNs i.e., local receptive field, prohibits the network to learn the distinctive global dependencies at initial layers. This study proposes a self-attention based deep architecture that learns global dependencies at each network layer to address CNN's limitation. Additionally, the introduction of a novel contextual learning module called Attention Drop Block (ADB) supports learning of less attentive areas of an image as well. The proposed model is evaluated on two public Re-ID benchmarks Market1501 and DukeMTMC-ReID, and outperformed all CNN baseline Re-ID models. The implementation and trained models are made publicly available at https://git.io/JYRE3.
{"title":"Unveiling the Potential of Vision Transformer Architecture for Person Re-identification","authors":"N. Perwaiz, M. Shahzad, M. Fraz","doi":"10.1109/INMIC56986.2022.9972908","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972908","url":null,"abstract":"Person re-identification (Re-ID) is a process to re-identify a person if he has been already seen by a camera network. Since start the convolutional neural networks (CNNs) are dominantly being used to solve the person Re-ID problem. The default limitation of CNNs i.e., local receptive field, prohibits the network to learn the distinctive global dependencies at initial layers. This study proposes a self-attention based deep architecture that learns global dependencies at each network layer to address CNN's limitation. Additionally, the introduction of a novel contextual learning module called Attention Drop Block (ADB) supports learning of less attentive areas of an image as well. The proposed model is evaluated on two public Re-ID benchmarks Market1501 and DukeMTMC-ReID, and outperformed all CNN baseline Re-ID models. The implementation and trained models are made publicly available at https://git.io/JYRE3.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128753508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972913
Alishba Laeeq, Masham Zahid, Abdulwadood Waseem, Muhammad Umair Arshad
Parts-of-Speech (POS) tagging is a highly encouraged research topic in the field of Natural Language Processing. POS entails numerous practical applications such as text indexing, information retrieval, corpus tagging for research, and linguistic work. This paper outlines multiple methods for part-of-speech tagging in Roman Urdu. Sufficient work and relevant required corpora are not available for Roman Urdu. We have identified that there are several parts-of-speech classes in the Urdu Language, with limited access to a well-annotated corpus. A manually verified corpus has been used to evaluate and report multiple methods for the said task. Our experiments deal with twenty-three unique parts-of-speech classes based on the contextual requirements of the Urdu Language. Our experiments include several methods built upon artificial neural networks, based on approaches such as multi-layered neural networks, feedback recurrent networks, and self-attention models. The corpus we used is not domain specific and covers several topics of Pakistani interest. Our experiments varied to a certain degree in the success demonstrated and outperformed numerous baseline models of machine learning and deep learning.
{"title":"Hybrid deep learning based POS tagger for Roman Urdu","authors":"Alishba Laeeq, Masham Zahid, Abdulwadood Waseem, Muhammad Umair Arshad","doi":"10.1109/INMIC56986.2022.9972913","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972913","url":null,"abstract":"Parts-of-Speech (POS) tagging is a highly encouraged research topic in the field of Natural Language Processing. POS entails numerous practical applications such as text indexing, information retrieval, corpus tagging for research, and linguistic work. This paper outlines multiple methods for part-of-speech tagging in Roman Urdu. Sufficient work and relevant required corpora are not available for Roman Urdu. We have identified that there are several parts-of-speech classes in the Urdu Language, with limited access to a well-annotated corpus. A manually verified corpus has been used to evaluate and report multiple methods for the said task. Our experiments deal with twenty-three unique parts-of-speech classes based on the contextual requirements of the Urdu Language. Our experiments include several methods built upon artificial neural networks, based on approaches such as multi-layered neural networks, feedback recurrent networks, and self-attention models. The corpus we used is not domain specific and covers several topics of Pakistani interest. Our experiments varied to a certain degree in the success demonstrated and outperformed numerous baseline models of machine learning and deep learning.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134457441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972915
Abdul Rehman, L. Fahad
Daily gun and knife related incidents are increasing due to lack of security check. In most of the places CCTV cameras are being installed however they require surveillance all the time. It is difficult due to limitations of humans in vigilant monitoring of the surveillance videos. The need of automated weapon detection is evident to limit and reduce these types of incidents. The proposed approach is mainly focused on developing an automated weapon detection system to detect different types of firearms and knives. In order to detect these types of incidents, we used a YOLOv5 deep learning model on a self collected dataset. The evaluation of the proposed approach shows its ability in the accurate detection of these weapons with an F1 score of 0.95 in CCTV video.
{"title":"Real-Time Detection of Knives and Firearms using Deep Learning","authors":"Abdul Rehman, L. Fahad","doi":"10.1109/INMIC56986.2022.9972915","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972915","url":null,"abstract":"Daily gun and knife related incidents are increasing due to lack of security check. In most of the places CCTV cameras are being installed however they require surveillance all the time. It is difficult due to limitations of humans in vigilant monitoring of the surveillance videos. The need of automated weapon detection is evident to limit and reduce these types of incidents. The proposed approach is mainly focused on developing an automated weapon detection system to detect different types of firearms and knives. In order to detect these types of incidents, we used a YOLOv5 deep learning model on a self collected dataset. The evaluation of the proposed approach shows its ability in the accurate detection of these weapons with an F1 score of 0.95 in CCTV video.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125759247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972968
Mamoona Tasadduq
Roman Urdu is an informal form of writing the Urdu language which is written in Latin script. It is the language most widely used on the internet, social media, and text messaging by native Urdu speakers. The problem that arises with Roman Urdu is an inconsistent way of writing by different people. No standard rules are defined for writing Roman Urdu which makes it very difficult to perform Natural Language Processing. To overcome this issue, the text needs to be normalized to perform effective analysis. Therefore, this work provides a Roman Urdu dictionary that works as the foundation for processing Roman Urdu. It also proposes a model for the lexical normalization of Roman Urdu text.
{"title":"Lexical Normalization of Roman Urdu","authors":"Mamoona Tasadduq","doi":"10.1109/INMIC56986.2022.9972968","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972968","url":null,"abstract":"Roman Urdu is an informal form of writing the Urdu language which is written in Latin script. It is the language most widely used on the internet, social media, and text messaging by native Urdu speakers. The problem that arises with Roman Urdu is an inconsistent way of writing by different people. No standard rules are defined for writing Roman Urdu which makes it very difficult to perform Natural Language Processing. To overcome this issue, the text needs to be normalized to perform effective analysis. Therefore, this work provides a Roman Urdu dictionary that works as the foundation for processing Roman Urdu. It also proposes a model for the lexical normalization of Roman Urdu text.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124822154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972989
Afaq Ahmad Khan, A. Hassan, Muhammad Talha Jahangir
Machine learning (ML) has no doubt virtually helped in nearly all fields of life, including medical sciences. ML models are now being trained, tested and developed with the help of information gained from Electroencephalogram (EEG) Signals. Neural Networks (NN) are being used specifically in this regard to exploit their image classification ability. A special class of NN called Transfer Learning (TL), is used to enhance the capability of NNs. In this paper, EEG signals are extracted and used to classify Left or Right Motor Images of the brain using Inception V3 and VGG 16 models. We try to enhance the accuracy of these TL Models by exploiting a different methodology as compared to other available statistical methods available in the research community. For the said purpose, a dataset from Brain-Computer Interface (BCI) Competition IV 2b was used. EEG signals are extracted and transformed into Short Time Fourier Transform (STFT) images. These STFT images are labeled with either Left or Right Motor Imagery (MI) Class. The transfer learning models are trained using these STFT images and results are also compared with a state-of-the art research, implementing Capsule Networks.
{"title":"Subject Wise Motor Imagery Classification from EEG Data Using Transfer Learning","authors":"Afaq Ahmad Khan, A. Hassan, Muhammad Talha Jahangir","doi":"10.1109/INMIC56986.2022.9972989","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972989","url":null,"abstract":"Machine learning (ML) has no doubt virtually helped in nearly all fields of life, including medical sciences. ML models are now being trained, tested and developed with the help of information gained from Electroencephalogram (EEG) Signals. Neural Networks (NN) are being used specifically in this regard to exploit their image classification ability. A special class of NN called Transfer Learning (TL), is used to enhance the capability of NNs. In this paper, EEG signals are extracted and used to classify Left or Right Motor Images of the brain using Inception V3 and VGG 16 models. We try to enhance the accuracy of these TL Models by exploiting a different methodology as compared to other available statistical methods available in the research community. For the said purpose, a dataset from Brain-Computer Interface (BCI) Competition IV 2b was used. EEG signals are extracted and transformed into Short Time Fourier Transform (STFT) images. These STFT images are labeled with either Left or Right Motor Imagery (MI) Class. The transfer learning models are trained using these STFT images and results are also compared with a state-of-the art research, implementing Capsule Networks.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129951214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972919
Muhammad Burhan, Ahmad Arsalan, R. A. Rehman
The future Internet architecture, like Named-Data Network (NDN), converts host-based network structures into content-based network structures. Through this transformation, the overall network performance and efficiency are increased. Furthermore, every router in the NDN uses a caching mechanism. Thus, the cache replacement policy used by the NDN routers is also a significant determinant of the NDN's overall performance. Therefore, several types of research have been conducted about the NDN's cache replacement policy. In this article, a light-weighted cache replacement strategy is proposed that overcomes the limitations and drawbacks of the Least Frequently Used (LFU) cache policy. The proposed strategy applies variables, based on real-time producer popularity. Additionally, it can be observed through extensive simulations that the proposed strategy provides better results and shows a higher cache hit ratio as compared to the existing cache policies.
{"title":"EPP-LFU: An Efficient Producer Popularity-based LFU Policy for the Applications of Named-Data Network","authors":"Muhammad Burhan, Ahmad Arsalan, R. A. Rehman","doi":"10.1109/INMIC56986.2022.9972919","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972919","url":null,"abstract":"The future Internet architecture, like Named-Data Network (NDN), converts host-based network structures into content-based network structures. Through this transformation, the overall network performance and efficiency are increased. Furthermore, every router in the NDN uses a caching mechanism. Thus, the cache replacement policy used by the NDN routers is also a significant determinant of the NDN's overall performance. Therefore, several types of research have been conducted about the NDN's cache replacement policy. In this article, a light-weighted cache replacement strategy is proposed that overcomes the limitations and drawbacks of the Least Frequently Used (LFU) cache policy. The proposed strategy applies variables, based on real-time producer popularity. Additionally, it can be observed through extensive simulations that the proposed strategy provides better results and shows a higher cache hit ratio as compared to the existing cache policies.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127507935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972975
Noman Khan, Sohail Ahmed Khan, Muhammad Osama Afridi, Tanveer Abbas
Resonant converter based power supplies are widely deployed in many industrial applications for their high efficiency and high power density. For control and regulation of the output voltage in resonant converters, frequency modulation (FM) is an intuitive and preferred method. However, the control-input-to-output transfer function is non-linear function of the operating frequency. The mean operating frequency determines the DC gain and bandwidth of the system for small signal perturbations. In this regard, third order non-linear model for LLC resonant converters is proposed in the literature. At a fixed operating point (i.e. a fixed output voltage corresponding to a particular operating frequency), the model can be treated as fairly linear for small perturbations. It is observed that the DC gain and bandwidth at different operating points is quite different, so deciding an appropriate operating point for a particular application is an important design choice. This research considers the small signal model of an 8.8kV/2A LLC resonant converter designed with resonant frequency ($f_{r}$) of 22.7kHz and quality factor (Q)=4 for an industrial magnetron as a specific load. The model is validated through simulations and hardware experiments using the LLC resonant converter. For our specific system DC gain at $f_{r}$ is - 65dB and bandwidth is 310Hz. DC gain and bandwidth of the system at different operating frequencies give an insight to decide the appropriate operating point. Hence, this work offers a strong foundation for a controller design for the LLC resonant converter under consideration.
{"title":"Validation of Small Signal Model of an LLC Resonant Converter Based HVDC Modulator","authors":"Noman Khan, Sohail Ahmed Khan, Muhammad Osama Afridi, Tanveer Abbas","doi":"10.1109/INMIC56986.2022.9972975","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972975","url":null,"abstract":"Resonant converter based power supplies are widely deployed in many industrial applications for their high efficiency and high power density. For control and regulation of the output voltage in resonant converters, frequency modulation (FM) is an intuitive and preferred method. However, the control-input-to-output transfer function is non-linear function of the operating frequency. The mean operating frequency determines the DC gain and bandwidth of the system for small signal perturbations. In this regard, third order non-linear model for LLC resonant converters is proposed in the literature. At a fixed operating point (i.e. a fixed output voltage corresponding to a particular operating frequency), the model can be treated as fairly linear for small perturbations. It is observed that the DC gain and bandwidth at different operating points is quite different, so deciding an appropriate operating point for a particular application is an important design choice. This research considers the small signal model of an 8.8kV/2A LLC resonant converter designed with resonant frequency ($f_{r}$) of 22.7kHz and quality factor (Q)=4 for an industrial magnetron as a specific load. The model is validated through simulations and hardware experiments using the LLC resonant converter. For our specific system DC gain at $f_{r}$ is - 65dB and bandwidth is 310Hz. DC gain and bandwidth of the system at different operating frequencies give an insight to decide the appropriate operating point. Hence, this work offers a strong foundation for a controller design for the LLC resonant converter under consideration.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128330804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972955
Iqra Ali, M. Naeem
With immense population growth in recent years, social data is growing at a rapid pace, which in turn can prove to be a rich source of hidden information. This work focuses on identifying user interest in electronic products, especially smartphones, using social data. This will help electronic businesses in the personalized marketing of their products. From the literature, most of the existing approaches attempted to identify user interest based on their ratings. In our understanding, the contents of reviews are equally important in identifying people's interests. Therefore, in this paper, we proposed a framework that identifies user interests based on their reviews and their ratings. Moreover, it performs an analysis of the aforementioned reviews, and profiles user interest. To achieve this, we used website data, written in the Roman Urdu language. To the best of our knowledge, very limited research has been carried out on the Roman Urdu dataset, as it is considered a low-resource language. Concerning our methodology, we first performed topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid of both. Based on the identified topics, we performed user interest profiling based on the probabilities of each model/brand using the Top2Vec model. We compared our results of topic modeling using reviews and reviews plus ratings. For topic modeling, we measure coherence score which we observe 52% for the hybrid approach while 47% and 45% for “BERT” and “LDA” respectively. Finally, For topic modeling, we perform human-based validation by comparing human-identified topics with the ones identified by our model.
{"title":"Identifying and Profiling User Interest over time using Social Data","authors":"Iqra Ali, M. Naeem","doi":"10.1109/INMIC56986.2022.9972955","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972955","url":null,"abstract":"With immense population growth in recent years, social data is growing at a rapid pace, which in turn can prove to be a rich source of hidden information. This work focuses on identifying user interest in electronic products, especially smartphones, using social data. This will help electronic businesses in the personalized marketing of their products. From the literature, most of the existing approaches attempted to identify user interest based on their ratings. In our understanding, the contents of reviews are equally important in identifying people's interests. Therefore, in this paper, we proposed a framework that identifies user interests based on their reviews and their ratings. Moreover, it performs an analysis of the aforementioned reviews, and profiles user interest. To achieve this, we used website data, written in the Roman Urdu language. To the best of our knowledge, very limited research has been carried out on the Roman Urdu dataset, as it is considered a low-resource language. Concerning our methodology, we first performed topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid of both. Based on the identified topics, we performed user interest profiling based on the probabilities of each model/brand using the Top2Vec model. We compared our results of topic modeling using reviews and reviews plus ratings. For topic modeling, we measure coherence score which we observe 52% for the hybrid approach while 47% and 45% for “BERT” and “LDA” respectively. Finally, For topic modeling, we perform human-based validation by comparing human-identified topics with the ones identified by our model.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131279492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972936
Mahnoor Aftab, Noreen Jamil
The use of technology is increasing day by day as it is helping in daily life issues in lesser time. The children these days prefer using technology more than any other medium of learning. Many researchers have incorporated gamification in educational application to enhance the value of such applications and to attract students to use the application which in turn enhance their learning performance. This research focuses on the children learning Qaida applications which involve gamification so that children can have more attraction and interest in learning the most important Islamic religious book Quran. The comparison of different gaming elements in m- learning applications is done and included in a prototype of Gamified Quran. The prototype has been tested by an experiment and the output of learning performance has been measured with the help of multiple tests and it turned out to have positive impact on learning performance of the children.
{"title":"Evaluating the Impact of Gamified Quranic Learning Mobile Apps for Children","authors":"Mahnoor Aftab, Noreen Jamil","doi":"10.1109/INMIC56986.2022.9972936","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972936","url":null,"abstract":"The use of technology is increasing day by day as it is helping in daily life issues in lesser time. The children these days prefer using technology more than any other medium of learning. Many researchers have incorporated gamification in educational application to enhance the value of such applications and to attract students to use the application which in turn enhance their learning performance. This research focuses on the children learning Qaida applications which involve gamification so that children can have more attraction and interest in learning the most important Islamic religious book Quran. The comparison of different gaming elements in m- learning applications is done and included in a prototype of Gamified Quran. The prototype has been tested by an experiment and the output of learning performance has been measured with the help of multiple tests and it turned out to have positive impact on learning performance of the children.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132969080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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