2021 4th International Conference on Computing & Information Sciences (ICCIS)最新文献

{"title":"Deep Frustration Severity Network for the Prediction of Declined Students' Cognitive Skills","authors":"Sadique Ahmad, M. Anwar, Mir Ahmad Khan, M. Shahzad, Mansoor Ebrahim, Imran Memon","doi":"10.1109/ICCIS54243.2021.9676396","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676396","url":null,"abstract":"Prediction of declined Cognitive Skills (CS) is essential not only for students and tutors but also for policymakers to make appropriate policies for the effective educational systems, such as the evaluation of admission criteria, teaching method, class activities, and examination systems. Articles are saturated with the number of findings which statistically correlate students' weak CS with the influence of frustration severity. Prior approaches have predicted declined CS using biological factors and study-related attributes of a student. Nevertheless these studies are insufficient to predict students' CS using the adverse influence of frustration severity. In this work, we have proposed Deep Frustration Severity Network and the loopholes in the existing approaches are the primary source of inspiration for this network. The proposed network has four outer layers for frustration severity while 34 inner layers for the CS outcomes. During outer and inner iterations student's CS is iteratively estimated while considering the adverse influence of frustration severity layers. Eventually the deep network predicts declined students' CS with iterative calculation process. We have validated the network on a students' score dataset. It achieved significant results in terms of state-of-the-art evaluation measures.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126358097","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":"Techniques and Methodologies of Self-Adaptive Software and its Architecture: A Survey","authors":"Abdur Rehman Riaz, Asma Rauf, S. M. Gilani, Muhammad Bilal Bashir","doi":"10.1109/ICCIS54243.2021.9676190","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676190","url":null,"abstract":"Nowadays, due to the continuously changing en-vironment, modern software systems can work in a dynamic environment. But many changes occur at runtime so, to handle the changes self-adaptive mechanism plays an important role. To encounter the requirement of rapid application development adaptive software is used widely. Adaptive software architecture is based on three steps speculate, collaborate, learn. There are a lot of techniques for adaptive software architecture most appropriate technique is selected by comparing them. In this paper, we perform a survey on different research papers related to the self-adaptive system and self-adaptive architecture. After studied the techniques gaps in the literature are identified and compare those techniques. Different common parameters are identified and considered by studying literature. Then these parameters are used to analyze survey techniques which is helpful for researchers and future directions in this area.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122317383","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":"Sentiment Analysis of Cancer Patients About Their Treatment During the Peak Time of Pandemic COVID-19","authors":"Bilal Ahmad, Sun Jun","doi":"10.1109/ICCIS54243.2021.9676393","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676393","url":null,"abstract":"Social media has become a valuable tool for users to express their opinion and for researchers to analyze public sentiment more efficiently. People respond much quickly towards any issue on social than any other traditional platform. Observing the patient's opinion on social media about the hospital medical facilities is a new trend that several hospitals are adopting recently in the modern world to improve their heal care facilities. After the pandemic of SARS-CoV-2, it has influenced the health care practices of all the world. Initial investigations indicate that patients with comorbidities are more fragile to this SARS-CoV-2 infection. Medical experts suggested postponing the routine treatment of cancer patients. However, few meta-analyses suggested evidence are not sufficient to hold the claim of the frailty of cancer patients to COVID-19. They were not in favor of shelving the scheduled treatments. On the other hand, some medical experts favored postponing cancer patients' scheduled treatments like chemotherapy, which could be a dangerous decision for cancer patients. We conducted the sentiment analysis of the patients with various comorbidities (diseases like diabetes, obesity, and cancer in which patient has to visit the hospital more often) to understand their point of view whether they were satisfied during the pandemic with their treatment or not? How Covid-19 affected their scheduled appointments. To serve the purpose, we gathered more than 150000 relevant tweets from Twitter (Jan 2020 to April 2020) to analyze the sentiment of cancer patients around the world. Our findings demonstrate a surge in the argument about cancer and its treatment after the outbreak of COVID-19. Most of the tweets are reasonable (52.6%) compared to negative ones (24.3%). We developed polarity and subjectivity distribution to better recognize the positivity/negativity in the sentiment. The results reveal that the polarity range of positive tweets is within the range of 0 to 0.5. That means the tendency in the tweets is not negative (it is above zero) nor so much positive too. It is statistical evidence supporting how natural language processing (NLP) can be used to better understand the patient's behavior in real-time. It may facilitate the medical professionals to make better decisions to organize the routine management of cancer patients.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121469755","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":"Discriminating plasma membrane, internal membrane, and organelle membrane proteins by SVM","authors":"Mehwish Faiz, A. Ahmed, S. Abid","doi":"10.1109/ICCIS54243.2021.9676407","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676407","url":null,"abstract":"Different membrane proteins are associated with the bio-membrane of distinct organelles which characterize their activities in and out of the cell based on their locale. Recognition of the cellular alcove is thus crucial and can be executed through machine learning algorithms. We implemented a Support Vector Machine(SVM) in order to reveal the type of membrane protein by taking the Mem Loci dataset. Before implementing SVM, we transformed the amino acid sequence into Pseudo amino acid composition and this yields an accuracy of 74%. For output, we have to enter the PseAAC of proteins and the screen reveal the category of membrane protein as either internal membrane protein or organelle membrane protein, or plasma membrane protein.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115009007","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 Convolutional Neural Network-based Framework for the Assessment of Human Muscles","authors":"Noman Ali, Muhammad Abubakr, M. Shaikh, A. Shahid, Wayne C. K. Poon, Rizwan Qureshi","doi":"10.1109/ICCIS54243.2021.9676387","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676387","url":null,"abstract":"This paper presents a system for assessing men's physique, using advanced computer vision and deep learning methods. The pipeline involves the segmenting and gauging of the condition of various muscle groups. The proposed system is composed of two different deep learning models working in tandem, the first is an Instance Segmentation Mask R-CNN and the second is GoogLeNet. The Mask R-CNN is used for the accurate multi-class identification and detection of different muscle groups, such as chest, biceps, abs, and shoulders. GoogLeNet then further classifies each muscle group into various levels (level 1, level 2, up to level n). Furthermore, performance metrics, such as accuracy, precision, F1-measure and confusion matrices are used to evaluate the effectiveness of the proposed system. We believe, that as this system provides information regarding the physical shape/ form of a person, it can be used to augment Diet and Exercise Recommendation Systems (DERS) and can have many commercial as well as clinical applications.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133792393","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":"Multiclass Imbalanced Classification of Quranic Verses Using Deep Learning Approach","authors":"Aqsa Noor, Ahmad Ali","doi":"10.1109/ICCIS54243.2021.9676386","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676386","url":null,"abstract":"This paper uses deep learning approach for the classification of Quranic verses. The dataset has an imbalance, hence first it is balanced by oversampling. This paper aims to classify the verses using Bidirectional Encoder Representation from Transformers (BERT) word embedding by considering the context of words. BERT reads a word with all its neighboring words and assigns representations accordingly. Furthermore, to ensure that the classifier remembers the most important part of the input sequence, deep learning classifiers with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used for classification. As the BERT word embeddings of the text data are created, they are fed to 3 Neural Network (NN) models i.e. NN with LSTM which achieved f1-score and accuracy of 0.85 for uncased, and f1-score of 0.82 and accuracy of 0.83 for cased embedding; NN with GRU which achieved F1-score and accuracy of 0.89 for uncased and 0.90 for cased embedding; and fine-tuned BERT model which achieved F1-scores of 0.93 and accuracy of 0.98 for both base-uncased and base-cased embedding.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130908380","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":"Vulnerabilities and Security Threats for IoT in Transportation and Fleet Management","authors":"Ayima Zahra, Muhammad Asif, A. A. Nagra, Muhammad Azeem, S. A. Gilani","doi":"10.1109/ICCIS54243.2021.9676388","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676388","url":null,"abstract":"The fields of transportation and fleet management have been evolving at a rapid pace and most of these changes are due to numerous incremental developments in the area. However, a comprehensive study that critically compares and contrasts all the existing techniques and methodologies in the area is still missing. This paper presents a comparative analysis of the vulnerabilities and security threats for IoT and their mitigation strategies in the context of transportation and fleet management. Moreover, we attempt to classify the existing strategies based on their underlying principles.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133654349","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":"An Overview of Requirements Elicitation Techniques in Software Engineering with a focus on Agile Development","authors":"Saman Tariq, Ahmad Ibrahim, Ali Usama, M. Shahbaz","doi":"10.1109/ICCIS54243.2021.9676192","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676192","url":null,"abstract":"In software requirement engineering, main focus is on eliciting information and extracting the main requirements of user. For extracting the requirement of the user, selection of appropriate tool/technique for specific situation is mandatory. Requirement elicitation is a difficult phase of requirement engineering. Elicitation includes traditional, collaboration, cognitive, innovative and contextual techniques. According to the nature of project and situation, one elicitation technique is not sufficient. This paper inspects that how requirement elicitation and its different tools/techniques work and what are the drawbacks of tools/techniques on the basis of particular situation that are demanded by the client. Agile model is the most common model of software development. Therefore, in this study tools/techniques of elicitation that are used in agile development phase have also been discussed in detail. This paper helps the analyst and project managers to select the appropriate tool and technique for a particular situation.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159475","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":"Development of Real-Time Eye Tracking Algorithm","authors":"Siti Nuradlin Syahirah Sheikh Anwar, A. A. Aziz, Syed Hasan Adil","doi":"10.1109/ICCIS54243.2021.9676406","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676406","url":null,"abstract":"This project develops a real-time eye tracking algorithm to improve the accuracy of iris detection and gaze classification. Eye tracking has been utilized in many applications to detect driver awareness, predict human behaviour, and assist paralyzed individuals. Detecting the centre of iris is a crucial step in eye tracking development and Conventional Circular Hough Transform (CCHT) is widely used for the iris detection. However, the accuracy of the CCHT method decreases when the head of an object is not orthogonally positioned to the camera under ambient light changes. To overcome this problem, the facial landmark detector is implemented to detect the eyes as the Region of Interest (ROI), track the eyes and identify the gaze by classifying the eye positions to left, right and middle. The eye tracking algorithm has an added feature to detect eye blinking for drowsiness detection. To gain better accuracy, the classifier uses a scan method to classify the eye position based on the levels of pixel intensity. The eye tracking algorithm is implemented in OpenCV using Python Software for ease of portability. The results show that the average accuracy of 100% and 90% are achieved in the iris detection and gaze position classification, respectively.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121866827","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":"Accessibility of Social Media Application for Blinds","authors":"Mamoona Atif Swati, Tahir Mustafa Madni, U. Janjua, Iftikhar Ahmad","doi":"10.1109/ICCIS54243.2021.9676390","DOIUrl":"https://doi.org/10.1109/ICCIS54243.2021.9676390","url":null,"abstract":"With the increase in technology, social networking became an essential component of human life. People communicate and share information through social networking applications, such as Facebook, Twitter and Instagram. However, it is observed that accessing such applications for visually impaired or blind users is challenging and such applications are reported as highly inaccessible. This study aims to analyze the usefulness of Facebook Messenger application for blind users using smartphone devices. Initially, an experiment is conducted with five blind people and their interaction with existing Facebook Messenger application is observed. Finally, the future work is suggested based on the obtained findings in the design of new social networking application. Further it will be compared with available applications to enhance the usability for blind users.","PeriodicalId":165673,"journal":{"name":"2021 4th International Conference on Computing & Information Sciences (ICCIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132190256","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}