Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581825
M. Hammad, W. Elmedany, Y. Ismail
Broadcasting applications such as video surveillance systems are using High Definition (HD) videos. The use of high-resolution videos increases significantly the data volume of video coding standards such as High-Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC), which increases the challenge for storing, processing, encrypting, and transmitting these data over different communication channels. Video compression standards use state-of-the-art techniques to compress raw video sequences more efficiently, such techniques require high computational complexity and memory utilization. With the emergent of using HEVC and video surveillance systems, many security risks arise such as man-in-the-middle attacks, and unauthorized disclosure. Such risks can be mitigated by encrypting the traffic of HEVC. The most widely used encryption algorithm is the Advanced Encryption Standard (AES). Most of the computational complexity in AES hardware-implemented is due to S-box or sub-byte operation and that because it needs many resources and it is a non-linear structure. The proposed AES S-box ROM design considers the latest HEVC used for homeland security video surveillance systems. This paper presents different designs for VHDL efficient ROM implementation of AES S-box using IP core generator, ROM components, and using Functions, which are all supported by Xilinx. IP core generator has Block Memory Generator (BMG) component in its library. S-box IP core ROM is implemented using Single port block memory. The S-box lookup table has been used to fill the ROM using the .coe file format provided during the initialization of the IP core ROM. The width is set to 8-bit to address the 256 values while the depth is set to 8-bit which represents the data filed in the ROM. The whole design is synthesized using Xilinx ISE Design Suite 14.7 software, while Modelism (version10.4a) is used for the simulation process. The proposed IP core ROM design has shown better memory utilization compared to non-IP core ROM design, which is more suitable for memory-intensive applications. The proposed design is suitable for implementation using the FPGA ROM design. Hardware complexity, frequency, memory utilization, and delay are presented in this paper.
视频监控系统等广播应用正在使用高清(HD)视频。高分辨率视频的使用大大增加了视频编码标准的数据量，如高效视频编码(HEVC)和高级视频编码(AVC)，这增加了存储、处理、加密和在不同通信信道上传输这些数据的挑战。视频压缩标准使用最先进的技术来更有效地压缩原始视频序列，这些技术要求较高的计算复杂度和内存利用率。随着HEVC和视频监控系统的兴起，出现了中间人攻击、未经授权泄露等安全隐患。这种风险可以通过加密HEVC的流量来减轻。目前使用最广泛的加密算法是高级加密标准AES (Advanced encryption Standard)。AES硬件实现中的大部分计算复杂性是由于S-box或子字节操作，这是因为它需要很多资源，而且它是一个非线性结构。提出的AES S-box ROM设计考虑了用于国土安全视频监控系统的最新HEVC。本文介绍了Xilinx支持的基于IP核生成器、ROM组件和使用函数实现AES S-box的VHDL高效ROM实现的不同设计。IP核生成器在其库中具有块内存生成器(BMG)组件。S-box IP核ROM采用单端口块存储器实现。S-box查找表已用于使用IP核心ROM初始化期间提供的.coe文件格式填充ROM。宽度设置为8位以解决256个值，而深度设置为8位，表示ROM中提交的数据。整个设计使用Xilinx ISE design Suite 14.7软件合成，而Modelism (version10.4a)用于模拟过程。与非IP核ROM设计相比，所提出的IP核ROM设计显示出更好的内存利用率，更适合内存密集型应用。所提出的设计适合使用FPGA ROM设计实现。本文给出了硬件复杂度、频率、内存利用率和延迟。
{"title":"Design and Simulation of AES S-Box Towards Data Security in Video Surveillance Using IP Core Generator","authors":"M. Hammad, W. Elmedany, Y. Ismail","doi":"10.1109/3ICT53449.2021.9581825","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581825","url":null,"abstract":"Broadcasting applications such as video surveillance systems are using High Definition (HD) videos. The use of high-resolution videos increases significantly the data volume of video coding standards such as High-Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC), which increases the challenge for storing, processing, encrypting, and transmitting these data over different communication channels. Video compression standards use state-of-the-art techniques to compress raw video sequences more efficiently, such techniques require high computational complexity and memory utilization. With the emergent of using HEVC and video surveillance systems, many security risks arise such as man-in-the-middle attacks, and unauthorized disclosure. Such risks can be mitigated by encrypting the traffic of HEVC. The most widely used encryption algorithm is the Advanced Encryption Standard (AES). Most of the computational complexity in AES hardware-implemented is due to S-box or sub-byte operation and that because it needs many resources and it is a non-linear structure. The proposed AES S-box ROM design considers the latest HEVC used for homeland security video surveillance systems. This paper presents different designs for VHDL efficient ROM implementation of AES S-box using IP core generator, ROM components, and using Functions, which are all supported by Xilinx. IP core generator has Block Memory Generator (BMG) component in its library. S-box IP core ROM is implemented using Single port block memory. The S-box lookup table has been used to fill the ROM using the .coe file format provided during the initialization of the IP core ROM. The width is set to 8-bit to address the 256 values while the depth is set to 8-bit which represents the data filed in the ROM. The whole design is synthesized using Xilinx ISE Design Suite 14.7 software, while Modelism (version10.4a) is used for the simulation process. The proposed IP core ROM design has shown better memory utilization compared to non-IP core ROM design, which is more suitable for memory-intensive applications. The proposed design is suitable for implementation using the FPGA ROM design. Hardware complexity, frequency, memory utilization, and delay are presented in this paper.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134118733","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581890
Mohamed Almansoor, Y. Harrath
Automated Teller Machines (ATMs) often lack the required funds or become malfunctioned, which affects the customer experience and the reputation of the bank. Banks try to quickly resolve the problem through cash-in-transit companies that handle the operations of ATM refilling and maintenance. However, one of the largest dilemmas is to determine the order of visiting the ATMs as well as to balance the workload among the workforces during the day. In addition, there is a need to handle real-time and urgent requests during the day. This problem was modelled as a realtime multiple Travelling Salesmen Problem (mTSP). New constrains including traffic data, ATM priorities, and safety measurements were considered. We used big data analytics to extract useful features related to the customer withdrawal trends and active locations from real data provided by a Bahraini bank. To solve this NP-hard problem, we proposed a brute force method that generates optimal routes for limited-sized problem instances, up to 35 ATMs. Moreover, a greedy technique was proposed to solve large-sized instances considering one salesman. The obtained TSP route is then cut into clusters using unsupervised machine learning models. A modified version of k-Means has been applied with constrains to control the size of each cluster.
{"title":"Big Data Analytics, Greedy Approach, and Clustering Algorithms for Real-Time Cash Management of Automated Teller Machines","authors":"Mohamed Almansoor, Y. Harrath","doi":"10.1109/3ICT53449.2021.9581890","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581890","url":null,"abstract":"Automated Teller Machines (ATMs) often lack the required funds or become malfunctioned, which affects the customer experience and the reputation of the bank. Banks try to quickly resolve the problem through cash-in-transit companies that handle the operations of ATM refilling and maintenance. However, one of the largest dilemmas is to determine the order of visiting the ATMs as well as to balance the workload among the workforces during the day. In addition, there is a need to handle real-time and urgent requests during the day. This problem was modelled as a realtime multiple Travelling Salesmen Problem (mTSP). New constrains including traffic data, ATM priorities, and safety measurements were considered. We used big data analytics to extract useful features related to the customer withdrawal trends and active locations from real data provided by a Bahraini bank. To solve this NP-hard problem, we proposed a brute force method that generates optimal routes for limited-sized problem instances, up to 35 ATMs. Moreover, a greedy technique was proposed to solve large-sized instances considering one salesman. The obtained TSP route is then cut into clusters using unsupervised machine learning models. A modified version of k-Means has been applied with constrains to control the size of each cluster.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"551 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134292141","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581512
Hanan Sawalmeh, Manar Malayshi, S. Ahmad, Ahmed Awad
Through the COVID-19 pandemic, the number of clients using Virtual Private Network (VPN) has dramatically increased. Consequently, VPN vulnerabilities have become target points to be exploited by attackers. However, studies have been released to defend against such attacks with the purpose of securing VPN. Nevertheless, attacks with high sophistication still target VPNs to comprise the critical data being communicated. VPN servers use protocols to secure connections with clients. However, these protocols are still targeted specifically with Denial-of-Service (DoS) attacks. This paper analyzes and treats the vulnerability of key negotiation process in the main mode as well as aggressive mode of Internet Key Exchange (IKE) protocol in IP Security (IPsec) VPN. We demonstrate experiments of a DoS attack based on Open Shortest Path First (OSPF) protocol adjacent route spoofing. Thereafter, we propose a method to tackle those attacks through exploiting the Suricata as an Intrusion Detection System (IDS) in defending the VPN against DoS attacks.
{"title":"VPN Remote Access OSPF-based VPN Security Vulnerabilities and Counter Measurements","authors":"Hanan Sawalmeh, Manar Malayshi, S. Ahmad, Ahmed Awad","doi":"10.1109/3ICT53449.2021.9581512","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581512","url":null,"abstract":"Through the COVID-19 pandemic, the number of clients using Virtual Private Network (VPN) has dramatically increased. Consequently, VPN vulnerabilities have become target points to be exploited by attackers. However, studies have been released to defend against such attacks with the purpose of securing VPN. Nevertheless, attacks with high sophistication still target VPNs to comprise the critical data being communicated. VPN servers use protocols to secure connections with clients. However, these protocols are still targeted specifically with Denial-of-Service (DoS) attacks. This paper analyzes and treats the vulnerability of key negotiation process in the main mode as well as aggressive mode of Internet Key Exchange (IKE) protocol in IP Security (IPsec) VPN. We demonstrate experiments of a DoS attack based on Open Shortest Path First (OSPF) protocol adjacent route spoofing. Thereafter, we propose a method to tackle those attacks through exploiting the Suricata as an Intrusion Detection System (IDS) in defending the VPN against DoS attacks.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133131799","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581655
Gokul S R Nath, Jashaswimalya Acharjee, S. Deb
Due to the expeditious increase in the number of vehicles, there is an increase in the number of road casualties even in a highly sophisticated roadway. This depicts the natural limitation of a human in maintaining Traffic rules. To avoid any lethal circumstance assistive driving vehicles are introduced which consists of systems that guide drivers in different Traffic situations. Traffic sign recognition systems play a crucial role in assistive driving vehicles these systems have been based on characteristics of the sign and two-state detectors due to accuracy and real-time factors systems on these bases are not used for real-time application. In this paper, we present a system that can recognize Traffic signs and their distance from the vehicle in non-ideal lighting as well as in varying climatic conditions. Our work proceeds with the implementation of YOLOv3(deep convolutional network based on end-to-end detection algorithm) used for Traffic sign recognition and segmentation. Training of the model is done with GTSRB dataset and achieves an accuracy of about 98.5% for the recognition task in different real-time scenarios. Furthermore, an efficient Heuristic-based approach has been deployed for estimating the distance between the Traffic sign and the monocular camera(placed in the vehicle) at every instance.
{"title":"Traffic Sign Recognition and Distance Estimation with YOLOv3 model","authors":"Gokul S R Nath, Jashaswimalya Acharjee, S. Deb","doi":"10.1109/3ICT53449.2021.9581655","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581655","url":null,"abstract":"Due to the expeditious increase in the number of vehicles, there is an increase in the number of road casualties even in a highly sophisticated roadway. This depicts the natural limitation of a human in maintaining Traffic rules. To avoid any lethal circumstance assistive driving vehicles are introduced which consists of systems that guide drivers in different Traffic situations. Traffic sign recognition systems play a crucial role in assistive driving vehicles these systems have been based on characteristics of the sign and two-state detectors due to accuracy and real-time factors systems on these bases are not used for real-time application. In this paper, we present a system that can recognize Traffic signs and their distance from the vehicle in non-ideal lighting as well as in varying climatic conditions. Our work proceeds with the implementation of YOLOv3(deep convolutional network based on end-to-end detection algorithm) used for Traffic sign recognition and segmentation. Training of the model is done with GTSRB dataset and achieves an accuracy of about 98.5% for the recognition task in different real-time scenarios. Furthermore, an efficient Heuristic-based approach has been deployed for estimating the distance between the Traffic sign and the monocular camera(placed in the vehicle) at every instance.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584108","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581861
Jia Heng Ong, K. Chia
Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technology that is widely utilized in Brain Control Interface (BCI) applications. Feature extraction is crucial to remove unwanted signals and improve the accuracy of a machine learning algorithm in BCI. Despite principal component analysis (PCA) is a popular feature extraction method in near-infrared spectroscopy, PCA is rarely studied in fNIRS. Thus, this study compared fNIRS-based BCI models that used PCA and that used statistical features in BCI for four mental activities classification. First, PCA was applied to transform pre-processed fNIRS signals into few principal components that were the inputs of artificial neural network (ANN) to form PCs-ANN. Three different combinations of fNIRS signals were used to study the performance of PCs-ANN using 10-fold cross-validation. The best PCs-ANN was compared with ANN that used statistical-based features. The finding shows that PCs-ANN outperformed ANN that used statistical-based features in the BCI classification application.
{"title":"Principal Components-Artificial Neural Network in Functional Near-Infrared Spectroscopy (fNIRS) for Brain Control Interface","authors":"Jia Heng Ong, K. Chia","doi":"10.1109/3ICT53449.2021.9581861","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581861","url":null,"abstract":"Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technology that is widely utilized in Brain Control Interface (BCI) applications. Feature extraction is crucial to remove unwanted signals and improve the accuracy of a machine learning algorithm in BCI. Despite principal component analysis (PCA) is a popular feature extraction method in near-infrared spectroscopy, PCA is rarely studied in fNIRS. Thus, this study compared fNIRS-based BCI models that used PCA and that used statistical features in BCI for four mental activities classification. First, PCA was applied to transform pre-processed fNIRS signals into few principal components that were the inputs of artificial neural network (ANN) to form PCs-ANN. Three different combinations of fNIRS signals were used to study the performance of PCs-ANN using 10-fold cross-validation. The best PCs-ANN was compared with ANN that used statistical-based features. The finding shows that PCs-ANN outperformed ANN that used statistical-based features in the BCI classification application.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130894213","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581789
M. S. Sharif, Mina Moein
The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods.
{"title":"An Effective Cost-Sensitive Convolutional Neural Network for Network Traffic Classification","authors":"M. S. Sharif, Mina Moein","doi":"10.1109/3ICT53449.2021.9581789","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581789","url":null,"abstract":"The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115202144","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581841
Shaima Almeer, Fatema A. Albalooshi, Aysha Alhajeri
Locating oil spills is a crucial portion of an effective marine contamination administration. In this paper, we address the issue of oil spillage location exposure within the Arabian Gulf region, by leveraging a Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Custom Vision). Our workflow comprises of virtual machine, database, and four modules (Information Collection Module, Discovery Show, Application Module, and a Choice Module). The adequacy of the proposed workflow is assessed on Synthetic Aperture Radar (SAR) imagery of the targeted region. Qualitative and quantitative analysis show that the purposed algorithm can detect oil spill occurrence with an accuracy of 90.5%.
{"title":"Oil Spill Detection System in the Arabian Gulf Region: An Azure Machine-Learning Approach","authors":"Shaima Almeer, Fatema A. Albalooshi, Aysha Alhajeri","doi":"10.1109/3ICT53449.2021.9581841","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581841","url":null,"abstract":"Locating oil spills is a crucial portion of an effective marine contamination administration. In this paper, we address the issue of oil spillage location exposure within the Arabian Gulf region, by leveraging a Machine-Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine-Learning Service (Custom Vision). Our workflow comprises of virtual machine, database, and four modules (Information Collection Module, Discovery Show, Application Module, and a Choice Module). The adequacy of the proposed workflow is assessed on Synthetic Aperture Radar (SAR) imagery of the targeted region. Qualitative and quantitative analysis show that the purposed algorithm can detect oil spill occurrence with an accuracy of 90.5%.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115240339","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582144
Farheen Akram, Muhammad Abrar ul Haq, H. Malik, Nadeem Mahmood
While considering the challenges of online teaching in the current scenario of Covid-19 pandemic, the current study aimed to analyze the effect of student participation, teachers' skills and strategies, teacher training, teaching domain and teaching perception on effectiveness of online teaching. Primary data was used to test the proposed model of the study, data was collected through emails using convenience sampling from university teachers of Pakistan. Structural equation modelling technique was used through SmartPLS (v.3.3.3) to analyze the model of this research. Findings of the current study indicate that student participation, teachers' skills and strategies, teacher's training, teaching domain and teaching perception have a significant positive effect on effectiveness of online teaching. Hence, this study recommends that universities must have to focus on the individual teachers' need to make online teaching more effective.
{"title":"Effectiveness of Online Teaching during COVID-19","authors":"Farheen Akram, Muhammad Abrar ul Haq, H. Malik, Nadeem Mahmood","doi":"10.1109/3ICT53449.2021.9582144","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582144","url":null,"abstract":"While considering the challenges of online teaching in the current scenario of Covid-19 pandemic, the current study aimed to analyze the effect of student participation, teachers' skills and strategies, teacher training, teaching domain and teaching perception on effectiveness of online teaching. Primary data was used to test the proposed model of the study, data was collected through emails using convenience sampling from university teachers of Pakistan. Structural equation modelling technique was used through SmartPLS (v.3.3.3) to analyze the model of this research. Findings of the current study indicate that student participation, teachers' skills and strategies, teacher's training, teaching domain and teaching perception have a significant positive effect on effectiveness of online teaching. Hence, this study recommends that universities must have to focus on the individual teachers' need to make online teaching more effective.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123619786","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582113
Debosmit Neogi, Nataraj Das, S. Deb
A methodology of real time pose estimation, which is believed to mitigate many orthopaedic adversaries pertaining to wrong posture, has been illustrated in this paper. Vast array of problems get reported that are known to arise due to maintaining a wrong posture during exercising or performing yoga, for a prolonged period of time. Several developments were made with regard to this issue, yet a major drawback was the presumption that a person during exercising or performing yoga or any kind of gym sessions, will keep the camera facing only at a fixed pre-determined portrayal direction. The approach, towards this problem, mainly deals with precise ROI detection, correct identification of human body joints and tracking down the motion of the body, all in real time. A major step towards converging to the solution is determining the angular separation between the joints and comparing them with the ones desired. Another important facet of the stated methodology is analysis of performance of the deep neural architecture in different camera positions. This is a major bottleneck for many different models that are intended to track posture of a person in real time. All these operations are done efficiently, with an appropriate trade-off between time complexity and performance metrics. At the end a robust feedback based support system has been obtained, that performs significantly better than the state of the art algorithm due to the precise transformation of input color space, contributing significantly in the field of orthopaedics by providing a feasible solution to avoid body strain and unnecessary pressure on joints during exercise.
{"title":"FitNet: A deep neural network driven architecture for real time posture rectification","authors":"Debosmit Neogi, Nataraj Das, S. Deb","doi":"10.1109/3ICT53449.2021.9582113","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582113","url":null,"abstract":"A methodology of real time pose estimation, which is believed to mitigate many orthopaedic adversaries pertaining to wrong posture, has been illustrated in this paper. Vast array of problems get reported that are known to arise due to maintaining a wrong posture during exercising or performing yoga, for a prolonged period of time. Several developments were made with regard to this issue, yet a major drawback was the presumption that a person during exercising or performing yoga or any kind of gym sessions, will keep the camera facing only at a fixed pre-determined portrayal direction. The approach, towards this problem, mainly deals with precise ROI detection, correct identification of human body joints and tracking down the motion of the body, all in real time. A major step towards converging to the solution is determining the angular separation between the joints and comparing them with the ones desired. Another important facet of the stated methodology is analysis of performance of the deep neural architecture in different camera positions. This is a major bottleneck for many different models that are intended to track posture of a person in real time. All these operations are done efficiently, with an appropriate trade-off between time complexity and performance metrics. At the end a robust feedback based support system has been obtained, that performs significantly better than the state of the art algorithm due to the precise transformation of input color space, contributing significantly in the field of orthopaedics by providing a feasible solution to avoid body strain and unnecessary pressure on joints during exercise.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125374700","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 : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582107
A. Müngen, Iclal Cetin Tas
The number of digital platforms that use cloud systems with microsystem architectures has increased day by day. By using public cloud systems efficiently, costs and expenses can be significantly reduced. This study tries to determine the necessary resource for the website by examining user activities for cloud resources management. A successful estimating system is essential for adjusting the price/performance balance of resource management. In this study, more than 1.5 million user logs with 18 different features were collected. SVM RBF and decision tree forest have been applied for this data. This study is shown that the SVM RBF method modeled the service rush time with an approximately 95% success rate. With the study, it has been revealed that a sound cloud resources management system can a significant economic benefit by adjusting the number of resources according to rush time prediction.
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