2022 24th International Multitopic Conference (INMIC)最新文献

{"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}

{"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}

{"title":"Multi-Organ Plant Classification Using Deep Learning","authors":"Asfand Yar Ali, L. Fahad","doi":"10.1109/INMIC56986.2022.9972979","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972979","url":null,"abstract":"The variability in the shape and appearance of the same plant organs and similarity between organs of different plants results in fewer inter-class and high intra-class variations making organ-based plant classification a challenging problem. Classification of plants using a single organ may not be able to deal with these challenges. Thus the use of multiple organs can be more effective in improving the classification performance by learning different aspects of the same class. Existing approaches mainly focus on generic features of plants while ignoring features related to multiple organs. In the proposed approach, Convolutional Neural Network (CNN) is used to exploit the information of multiple organs instead of a single organ for the classification of plants. Moreover, the representation of minority classes is increased through DC GAN. The comparison of the proposed approach with the existing approaches on the publicly available PlantCLEF dataset shows its better performance in the accurate classification of plants.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"2 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":"116768776","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}

{"title":"A Systematic Review on Fully Automated Online Exam Proctoring Approaches","authors":"Taskeen Fatima, F. Azam, A. W. Muzaffar","doi":"10.1109/INMIC56986.2022.9972964","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972964","url":null,"abstract":"In the past few decades E-learning in higher education is increased and played a vital role in pandemics like COVID-19. Particularly, online examinations are conducted on e-learning platforms which leads to many security and cheating issues. For this reason, numerous research is available proposed methodologies and techniques for seamless execution of online examination. However, it is hard to find any study that provides the latest systematic literature review of anti-cheat or cheating prediction techniques and the approaches in the literature. We have analyzed 2223 studies. However, after applying inclusion and exclusion criteria 23 studies relevant studies are finalized. The review revealed that there are three types of proctoring, fully live online, recorded & reviewed and fully automated. This study provides a comparative analysis of online examination techniques & tools performed on 23 studies from the last five years 2017 to 2021. Furthermore, in this time duration five leading cheating prevention features are identified.14 important techniques which are mostly used in this time duration are found in which best frequent approach used in literature is NLP and 10 data sets including both public and private are identified. Proceeding toward the proposed solution, a total of 20 tools for the anti-cheat examinations are found. Almost 23 leading existing tools were found in the literature. To narrow down the criteria for adoption factor is analyzed and studies of the online anti-cheat examination solution adoption in different countries are also investigated. Finally, the overall cost of the e-learning infrastructure, specifically the conduction of examinations is determined by comparing the key factors of the global adoption with major online exam features.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"21 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":"116895191","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}

{"title":"Recognition of Faces Wearing Masks Using Skip Connection Based Dense Units Augmented With Self Restrained Triplet Loss","authors":"M. A. Nawshad, Zuhair Zafar, M. Fraz","doi":"10.1109/INMIC56986.2022.9972912","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972912","url":null,"abstract":"Facial recognition-based systems are the most efficient and cost-effective of all the contactless biometric verification systems available. But, in the COVID-19 scenario, the performance of available facial recognition systems has been affected badly due to the presence of masks on people's faces. Various studies have reported the degradation of the performance of facial recognition systems due to masks. Therefore, there is a need for improvement in the performance of currently available facial recognition algorithms. In this research, we propose using Skip Connection based Dense Unit (SCDU) trained with Self Restrained Triplet Loss, to handle the embeddings produced by existing facial recognition algorithms for masked images. The SCDU is trained to make facial embeddings for unmasked and masked images of the same identity similar, as well as, embeddings for unmasked and masked images of different identities dissimilar. We have evaluated our results on the LFW dataset with synthetic masks as well as the real-world masked face recognition dataset, i.e., MFR2 and achieved improvement in verification performance in terms of Equal Error Rate, False Match Rate, False Non-Match Rate, and Fisher discriminant ratio.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"103 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":"114613079","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}

{"title":"Plants Disease Classification using Deep Learning","authors":"Abdul Rehman, L. Fahad","doi":"10.1109/INMIC56986.2022.9972966","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972966","url":null,"abstract":"Early detection of plant disease is useful in reducing its rapid spread; however similar visual appearances of different plant diseases make it a challenging problem. In the proposed approach, we improve the performance of plant disease detection by learning the fine differences in the visual appearances of these different diseases. We used pre-processing, data augmentation, and deep learning for the classification of different categories of diseases in plants. The representation of minority classes with fewer images is improved using DC-GAN. Different CNN based deep learning techniques are applied for classification. The performance comparison of the proposed approach with existing approaches on a publicly available plant village dataset shows its superior performance with an accuracy of 97.2% and an F1 score of 0.97 for incorrect predictions of different plant diseases.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"6 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":"126560664","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}

{"title":"A Review of DDoS Attack Detection and Prevention Mechanisms in Clouds","authors":"Muhammad Tehaam, Sahar Ahmad, Hassan Shahid, Muhammad Suleman Saboor, Ayesha Aziz, K. Munir","doi":"10.1109/INMIC56986.2022.9972962","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972962","url":null,"abstract":"Cloud provides access to shared pool of resources like storage, networking, and processing. Distributed denial of service attacks are dangerous for Cloud services because they mainly target the availability of resources. It is important to detect and prevent a DDoS attack for the continuity of Cloud services. In this review, we analyze the different mechanisms of detection and prevention of the DDoS attacks in Clouds. We identify the major DDoS attacks in Clouds and compare the frequently-used strategies to detect, prevent, and mitigate those attacks that will help the future researchers in this area.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"8 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":"133455432","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}

{"title":"Energy Efficiency Issues in Android Application: A Literature Review","authors":"Obaid Ullah, Muhammad Hanan, Maryam Abdul Ghafoor","doi":"10.1109/INMIC56986.2022.9972939","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972939","url":null,"abstract":"In today's digital world, almost every person owns a smartphone device. Due to more emphasis on the functional aspect of an application, programmers often follow such practices that consume a lot of energy. Hence, the purpose of this literature review is to find such issues that can cause more energy consumption in the android applications along with finding their solutions from the literature. The literature review also includes year-wise and venue-wise paper distribution. Out of our initial 145 papers, we discarded 4 papers based on a duplicate study, then 100 papers were discarded on the title and abstract-based screening while 22 papers were discarded based on inclusion/exclusion and quality assurance criteria. A final of 19 studies were considered for this study and were read thoroughly. Our results reveal that bad programming practice was the most discussed issue (26%) while tool-related problems and patterns were the least discussed issues in the literature (15.7%). Tool-based solutions are discussed mostly (36.84%) while refactoring technique and applying other techniques are discussed least (10.5%) in the literature. The work is helpful for the researchers and developers as they can learn from this about the energy consumption reasons and their solutions.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"21 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":"129834647","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}

{"title":"Device Interoperability for Industrial IoT using Model-Driven Architecture","authors":"Anam Amjad, F. Azam, Muhammad Waseem Anwar","doi":"10.1109/INMIC56986.2022.9972976","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972976","url":null,"abstract":"Industrial Internet of Things (IIoT) is an emerging domain, converting common objects into connecting objects with ubiquitous internet access to automate industry. Due to different vendors, supporting different infrastructures, a set of communication protocols such as Zigbee, 6LowPAN, Wireless Fidelity (Wi-Fi), Hyper Text Transfer Protocol (HTTP), etc. are introduced for IIoT. Thus, a closed ecosystem for smart devices is created. Particularly, when two or more industrial IoT applications are developed using different application-layer protocols such as Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP), or MQ Telemetry Transport (MQTT), devices are called heterogeneous devices and interoperability becomes a major challenge. In the existing literature, device-level interoperability using different application-layer protocols is enhanced with the help of intermediators at the network layer which includes servers, brokers, or gateways/adapters to route communication. However, these intermediators lead to several other issues such as dependency on network layer components, load balancing, single point of failure, and scalability. Therefore, the interoperability issue needs to be addressed at the application layer using a device intermediator instead of utilizing network layer components. For this purpose, Model Driven Engineering (MDE) is selected because less attention is paid to IIoT interoperable solutions development using MDE. To bridge this gap, a Model Driven Architecture (MDA) based approach is proposed that reduces the processing time and effort to develop these IIoT interoperable systems. For this purpose, (i) a metamodel, (ii) a UML profile, and (iii) transformation rules are developed to make heterogenous application-layer protocols interoperable using devices as intermediator. The initial feasibility of the proposed solution is demonstrated through a real-world case study i.e., a smart city. Results show that a complete solution for interoperability at the application layer for industrial IoT is provided using MDA. It will help the practitioners to automate industry 4.0 using model-driven based system development.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"76 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":"116441996","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}

{"title":"A Robust Algorithm for Candidate Pruning and Feature Weight Generation in E-Recruitment System","authors":"Saeed Ur Rehman Bhatti, Shujaat Hussain, Kifayat-Ullah Khan","doi":"10.1109/INMIC56986.2022.9972934","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972934","url":null,"abstract":"Globalization, enhanced networking, and contemporary communication systems have resulted in a substantial increase in the number of resumes created. Processing this massive chunk manually is time-consuming. Multiple Criteria Decision Making (MCDM) techniques, such as “Analytic Hierarchy Process” (AHP), “The Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS), “VlseKriterijumska Optimizacija I Kompromisno Resenje” (VIKOR), and their state-of-the-art optimized variants, SlashRank, consider multiple factors and then rank the results accordingly to streamline the process. These algorithms primarily focus on reducing computational complexity, however, they ignore automated feature importance since they require manual features weights as an explicit input. In the end, this leads to a biased result and a decentralized method of hiring candidates. Our research addresses the human intervention observed in previously identified techniques and proposes a technique that automates candidate pruning, manual feature priority input, and generates new feature importance based on user feedback. Our approach can be decomposed into automated candidate pruning for the highest priority feature, candidate selection feedback, and trend-based feature weight generation to replicate actual recruitment feature priority fluctuations. Our algorithm demonstrates promising results in minimizing human biases and generating a dynamic trend of feature importance over time.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"59 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":"121778571","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}