Pub Date : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615304
Sabri Al-Shaibany, Akram A. Almohammedi, V. Shepelev, S. Darshi, A. Al-Hemyari, Abdaladeem A.A Alsharaby, Abdulmalek M. M Abdullah, Abdullatif S.M Alhadry, Ezzadeen A. D Alomary
Vehicular Ad-Hoc Networks (VANETs) are a sub form of Mobile Ad-Hoc Network that provide communication among vehicles (V2V) and vehicles to infrastructure (V2I). VANETs have been developed to offer reliable and efficient services on the roads. These services include safety applications (collision warning), and non-safety applications (video and voice). The IEEE 802.11p is an extension of the IEEE 802.11 standard to support wireless access into vehicular environments. However, the IEEE 802.11p standard does not perform well for VANETs under high traffic load and mobility. The is owing to the nature of contention-based channel access mechanism in IEEE 802.11p sharing a common radio frequency. The work in this paper presents a new scheme to improve the channel access coordination of 802.11p for V2I communication under high traffic and mobility. This scheme adaptively adjusts the contention window (CW) based on the times (deadlines) that the vehicles are about to exit the RoadSide Unit (RSU) coverage area. Priority service is given to vehicles with shorter deadlines and vice versa. The Network Simulator (NS-2) v.2.35 is used for simulation. According to simulation results, our proposed scheme outperforms the existing scheme in terms of throughput, packet loss ratio and packet delivery.
{"title":"Mobility-based Enhancement for Channel Coordination of IEEE 802.11p on Vehicular Ad-hoc Networks Over V2I","authors":"Sabri Al-Shaibany, Akram A. Almohammedi, V. Shepelev, S. Darshi, A. Al-Hemyari, Abdaladeem A.A Alsharaby, Abdulmalek M. M Abdullah, Abdullatif S.M Alhadry, Ezzadeen A. D Alomary","doi":"10.1109/ITSS-IoE53029.2021.9615304","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615304","url":null,"abstract":"Vehicular Ad-Hoc Networks (VANETs) are a sub form of Mobile Ad-Hoc Network that provide communication among vehicles (V2V) and vehicles to infrastructure (V2I). VANETs have been developed to offer reliable and efficient services on the roads. These services include safety applications (collision warning), and non-safety applications (video and voice). The IEEE 802.11p is an extension of the IEEE 802.11 standard to support wireless access into vehicular environments. However, the IEEE 802.11p standard does not perform well for VANETs under high traffic load and mobility. The is owing to the nature of contention-based channel access mechanism in IEEE 802.11p sharing a common radio frequency. The work in this paper presents a new scheme to improve the channel access coordination of 802.11p for V2I communication under high traffic and mobility. This scheme adaptively adjusts the contention window (CW) based on the times (deadlines) that the vehicles are about to exit the RoadSide Unit (RSU) coverage area. Priority service is given to vehicles with shorter deadlines and vice versa. The Network Simulator (NS-2) v.2.35 is used for simulation. According to simulation results, our proposed scheme outperforms the existing scheme in terms of throughput, packet loss ratio and packet delivery.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125849889","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 : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615344
Nawaf Aljubory, B. Khammas
Ransomware is one of the most serious threats which constitute a significant challenge in the cybersecurity field. The cybercriminals use this attack to encrypts the victim's files or infect the victim's devices to demand ransom in exchange to restore access to these files and devices. The escalating threat of Ransomware to thousands of individuals and companies requires an urgent need for creating a system capable of proactively detecting and preventing ransomware. In this research, a new approach is proposed to detect and classify ransomware based on three machine learning algorithms (Random Forest, Support Vector Machines , and Näive Bayes). The features set was extracted directly from raw byte using static analysis technique of samples to improve the detection speed. To offer the best detection accuracy, CF-NCF (Class Frequency - Non-Class Frequency) has been utilized for generate features vectors. The proposed approach can differentiate between ransomware and goodware files with a detection accuracy of up to 98.33 percent.
勒索软件是构成网络安全领域重大挑战的最严重威胁之一。网络犯罪分子利用这种攻击来加密受害者的文件或感染受害者的设备，以索要赎金来恢复对这些文件和设备的访问权限。勒索软件对成千上万的个人和公司的威胁不断升级，迫切需要创建一个能够主动检测和预防勒索软件的系统。在本研究中，提出了一种基于三种机器学习算法(随机森林、支持向量机和Näive贝叶斯)的勒索软件检测和分类新方法。采用样本静态分析技术直接从原始字节中提取特征集，提高了检测速度。为了提供最佳的检测精度，采用CF-NCF (Class Frequency - Non-Class Frequency)方法生成特征向量。该方法可以区分勒索软件和恶意软件文件，检测准确率高达98.33%。
{"title":"Hybrid Evolutionary Approach in Feature Vector for Ransomware Detection","authors":"Nawaf Aljubory, B. Khammas","doi":"10.1109/ITSS-IoE53029.2021.9615344","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615344","url":null,"abstract":"Ransomware is one of the most serious threats which constitute a significant challenge in the cybersecurity field. The cybercriminals use this attack to encrypts the victim's files or infect the victim's devices to demand ransom in exchange to restore access to these files and devices. The escalating threat of Ransomware to thousands of individuals and companies requires an urgent need for creating a system capable of proactively detecting and preventing ransomware. In this research, a new approach is proposed to detect and classify ransomware based on three machine learning algorithms (Random Forest, Support Vector Machines , and Näive Bayes). The features set was extracted directly from raw byte using static analysis technique of samples to improve the detection speed. To offer the best detection accuracy, CF-NCF (Class Frequency - Non-Class Frequency) has been utilized for generate features vectors. The proposed approach can differentiate between ransomware and goodware files with a detection accuracy of up to 98.33 percent.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125114974","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 : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615290
Maha Fathy, Mohamed Salah Abood, Mustafa Maad Hamdi
Since proposed, Cloud Radio Access Network (Cloud-RAN) gives a committed architecture suitable for fulfilling 5G networks' applications. Cloud-RAN can solve challenges related to ever-evolving networks' mobile operators and an ever-growing number of end-users. Cloud-RAN architecture maintains both profitability and quality of service (QoS) . In this paper, power consumption is jointly formulated as power minimization beamforming and RRHs selection problem. Using the conventional convex or heuristic optimization approaches to find optimal solutions is highly complex; hence, we introduce an Artificial Neural Network (ANN) - based optimization model that aims to optimize the active Remote Radio Heads (RRHs) numbers in remote network sites and the consumed power. The proposed model considers various signal to interference plus noise ratios per client and power consumption models. Specifically, the model uses an adopted Bi-Section Group Sparse Beamforming (GSBF) optimization algorithm to reach near optimum solutions. Obtained validated results encourage machine learning techniques to reduce both the complexity and power consumption in such an emerging area.
{"title":"Optimization of Energy-Efficient Cloud Radio Access Networks for 5G using Neural Networks","authors":"Maha Fathy, Mohamed Salah Abood, Mustafa Maad Hamdi","doi":"10.1109/ITSS-IoE53029.2021.9615290","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615290","url":null,"abstract":"Since proposed, Cloud Radio Access Network (Cloud-RAN) gives a committed architecture suitable for fulfilling 5G networks' applications. Cloud-RAN can solve challenges related to ever-evolving networks' mobile operators and an ever-growing number of end-users. Cloud-RAN architecture maintains both profitability and quality of service (QoS) . In this paper, power consumption is jointly formulated as power minimization beamforming and RRHs selection problem. Using the conventional convex or heuristic optimization approaches to find optimal solutions is highly complex; hence, we introduce an Artificial Neural Network (ANN) - based optimization model that aims to optimize the active Remote Radio Heads (RRHs) numbers in remote network sites and the consumed power. The proposed model considers various signal to interference plus noise ratios per client and power consumption models. Specifically, the model uses an adopted Bi-Section Group Sparse Beamforming (GSBF) optimization algorithm to reach near optimum solutions. Obtained validated results encourage machine learning techniques to reduce both the complexity and power consumption in such an emerging area.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127002687","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 : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615286
Pouria Khanzadi, Shirin Kordnoori, Zahra Vasigh, H. Mostafaei, Ehsan Akhtarkavan
Nowadays, cyber physical systems are playing an important role in human life in which they provide features that make interactions between human and machine easier. To design and analysis such systems, the main problem is their complexity. In this paper, we propose a description language for cyber physical systems based on stochastic processes. The proposed language is called SPDL (Stochastic Description Process Language). For designing SPDL, two main parts are considered for Cyber Physical Systems (CSP): embedded systems and physical environment. Then these parts are defined as stochastic processes and CPS is defined as a tuple. Syntax and semantics of SPDL are stated based on the proposed definition. Also, the semantics are defined as by set theory. For implementation of SPDL, dependencies between words of a requirements are extracted as a tree data structure. Based on the dependencies, SPDL is used for describing the CPS. Also, a lexical analyzer and a parser based on a defined BNF grammar for SPDL is designed and implemented. Finally, SPDL of CPS is transformed to NuSMV which is a symbolic model checker. The Experimental results show that SPDL is capable of describing cyber physical systems by natural language.
{"title":"A Cyber Physical System based Stochastic Process Language With NuSMV Model Checker","authors":"Pouria Khanzadi, Shirin Kordnoori, Zahra Vasigh, H. Mostafaei, Ehsan Akhtarkavan","doi":"10.1109/ITSS-IoE53029.2021.9615286","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615286","url":null,"abstract":"Nowadays, cyber physical systems are playing an important role in human life in which they provide features that make interactions between human and machine easier. To design and analysis such systems, the main problem is their complexity. In this paper, we propose a description language for cyber physical systems based on stochastic processes. The proposed language is called SPDL (Stochastic Description Process Language). For designing SPDL, two main parts are considered for Cyber Physical Systems (CSP): embedded systems and physical environment. Then these parts are defined as stochastic processes and CPS is defined as a tuple. Syntax and semantics of SPDL are stated based on the proposed definition. Also, the semantics are defined as by set theory. For implementation of SPDL, dependencies between words of a requirements are extracted as a tree data structure. Based on the dependencies, SPDL is used for describing the CPS. Also, a lexical analyzer and a parser based on a defined BNF grammar for SPDL is designed and implemented. Finally, SPDL of CPS is transformed to NuSMV which is a symbolic model checker. The Experimental results show that SPDL is capable of describing cyber physical systems by natural language.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132896013","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 : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615333
Hussein J. Khadim, Faik K. Obaed, Ziad T. Abd Ali
Air pollution levels have been rising around the world in recent years. Long-term pollution exposure causes a variety of ailments, including lung disease, heart disease, and eye irritation. The term “indoor air quality” refers to the building’s residents' air quality. Physical variables, chemical or gaseous pollutants, and biological factors. Toxic gases are likely to be present in any laboratory where experiments or research are carried out. These contaminants can harm the health of the people that are working there, and the important work that is being done in this environment remains unaffected. A web-based system for indoor air quality monitoring in lab IAQML is presented in this study. The project was established to keep track of air quality metrics in the lab environment like carbon dioxide, carbon monoxide, alcohol, phenol, toluene, LPG, benzene, ammonia, and methane, if not properly maintained, this can have an impact on the inhabitants' comfort, health, and indoor working conditions. In general, the proposed method involves a selection of metal oxide MQ-sensors, and a Wi-Fi module connected to an Arduino microcontroller. The measured data from sensors is calculated in ppm units and then displayed on the Android device. Also, gas data is sent to the webpage through the ThingSpeak platform dashboard. The system has a notification function to alert students and workers in the laboratory when measurements of air quality are above or below specified thresholds. On the other hand, this allows for a well-controlled and maintained the quality standard for indoor air pollutants.
{"title":"Application of MQ-Sensors to Indoor Air Quality Monitoring in Lab based on IoT","authors":"Hussein J. Khadim, Faik K. Obaed, Ziad T. Abd Ali","doi":"10.1109/ITSS-IoE53029.2021.9615333","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615333","url":null,"abstract":"Air pollution levels have been rising around the world in recent years. Long-term pollution exposure causes a variety of ailments, including lung disease, heart disease, and eye irritation. The term “indoor air quality” refers to the building’s residents' air quality. Physical variables, chemical or gaseous pollutants, and biological factors. Toxic gases are likely to be present in any laboratory where experiments or research are carried out. These contaminants can harm the health of the people that are working there, and the important work that is being done in this environment remains unaffected. A web-based system for indoor air quality monitoring in lab IAQML is presented in this study. The project was established to keep track of air quality metrics in the lab environment like carbon dioxide, carbon monoxide, alcohol, phenol, toluene, LPG, benzene, ammonia, and methane, if not properly maintained, this can have an impact on the inhabitants' comfort, health, and indoor working conditions. In general, the proposed method involves a selection of metal oxide MQ-sensors, and a Wi-Fi module connected to an Arduino microcontroller. The measured data from sensors is calculated in ppm units and then displayed on the Android device. Also, gas data is sent to the webpage through the ThingSpeak platform dashboard. The system has a notification function to alert students and workers in the laboratory when measurements of air quality are above or below specified thresholds. On the other hand, this allows for a well-controlled and maintained the quality standard for indoor air pollutants.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"os-11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133448012","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}
In this paper, a system for face detection based on the generalized BOW method is proposed. We have utilized the space pyramid matching (SPM) method to overcome the neglected problem of space order of BOW. In the feature extraction stage, we have used SIFT method which is resistant against local variations. Sparse presentations usually are linearly separable; hence in the proposed system, we have utilized the sparse codding method in the feature learning stage. In the polling stage, we have used maximum polling operation to reach a unified vector from multiple descriptor vectors. Finally, a support vector machine classifier is used to classify face descriptor vectors. Simulation results show high accuracy of classification (ACC=0.9952) and its resistivity against previous methods.
{"title":"Face recognition based on sparse coding using support vector machine classifier","authors":"Arian Yousefiankalareh, Taraneh Kamyab, Farzad Shahabi, Ehsan Salajegheh, Hossein Mirzanejad, Mahsa Madadi Masouleh","doi":"10.1109/ITSS-IoE53029.2021.9615322","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615322","url":null,"abstract":"In this paper, a system for face detection based on the generalized BOW method is proposed. We have utilized the space pyramid matching (SPM) method to overcome the neglected problem of space order of BOW. In the feature extraction stage, we have used SIFT method which is resistant against local variations. Sparse presentations usually are linearly separable; hence in the proposed system, we have utilized the sparse codding method in the feature learning stage. In the polling stage, we have used maximum polling operation to reach a unified vector from multiple descriptor vectors. Finally, a support vector machine classifier is used to classify face descriptor vectors. Simulation results show high accuracy of classification (ACC=0.9952) and its resistivity against previous methods.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124563072","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 : 2021-11-01DOI: 10.1109/itss-ioe53029.2021.9615279
{"title":"Program Abstract Book","authors":"","doi":"10.1109/itss-ioe53029.2021.9615279","DOIUrl":"https://doi.org/10.1109/itss-ioe53029.2021.9615279","url":null,"abstract":"","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116132635","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 : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615280
M. H. Ullah, M. Uddin, S. Z. Islam
The ultra-low-profile dualband planar quasiyagi (QY) antenna is proposed and investigated their bandwidth as well as gain characteristics which suitable for IoT application. The special characteristics of this antenna are encompassed 0.17λ×0.13λ radiating patch, as well as the ground plane, which has been reduced. The substrates comprise manually copper laminated thickness 1.25 mm and dielectric substrate εr is 4.5. Measurement results of input ports return loss (S11) represent the high peak of dual-band 27.78% from 0.8 GHz to 1.05 GHz and 23.4% obtained from 2.1 GHz to 2.65 GHz. The antennas maximum gain found 4.95 dBi and 7.26 dBi in correspondence to the lower and upper band, respectively. Excellent performance horizontal polarized broadside radiation characteristics with proper impedance matching, improve gain assures the proposed antenna promising candidate for IoT applications
{"title":"Ultra-Low Profile, Compact Quasi-Yagi Antenna Suitable for IoT Application","authors":"M. H. Ullah, M. Uddin, S. Z. Islam","doi":"10.1109/ITSS-IoE53029.2021.9615280","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615280","url":null,"abstract":"The ultra-low-profile dualband planar quasiyagi (QY) antenna is proposed and investigated their bandwidth as well as gain characteristics which suitable for IoT application. The special characteristics of this antenna are encompassed 0.17λ×0.13λ radiating patch, as well as the ground plane, which has been reduced. The substrates comprise manually copper laminated thickness 1.25 mm and dielectric substrate εr is 4.5. Measurement results of input ports return loss (S11) represent the high peak of dual-band 27.78% from 0.8 GHz to 1.05 GHz and 23.4% obtained from 2.1 GHz to 2.65 GHz. The antennas maximum gain found 4.95 dBi and 7.26 dBi in correspondence to the lower and upper band, respectively. Excellent performance horizontal polarized broadside radiation characteristics with proper impedance matching, improve gain assures the proposed antenna promising candidate for IoT applications","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"9 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116673837","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 : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615345
Shubhnoor Gill, N. Sharma, Chetan Gupta, Argha Samanta
Over decades the attendance of students has been taken using methods involving paper. The limitations of this method are widely known and clearly understood, it is time-consuming, prone to errors and there is always a chance of proxy attendance. Many techniques that are implemented in today’s time are vastly unreliable and are majorly inefficient, like biometrics and Radio Frequency Identification (RFID), more importantly when there is a pandemic that majorly spreads via touch. This clearly presents an opportunity in the field of facial feature detection and face recognition. We propose an effective and modish solution to mark attendance using the face recognition technique including Haar Cascade and Local Binary Pattern Histogram algorithms. The system will recognize the face of an individual or multiple students and compare them with the predefined face encoding to make a CSV file of attendees with their details. To create the database we will use image augmentation techniques. This system can also be used to tackle the problem of fake attendance and proxies.
{"title":"Attendance Management System Using Facial Recognition and Image Augmentation Technique","authors":"Shubhnoor Gill, N. Sharma, Chetan Gupta, Argha Samanta","doi":"10.1109/ITSS-IoE53029.2021.9615345","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615345","url":null,"abstract":"Over decades the attendance of students has been taken using methods involving paper. The limitations of this method are widely known and clearly understood, it is time-consuming, prone to errors and there is always a chance of proxy attendance. Many techniques that are implemented in today’s time are vastly unreliable and are majorly inefficient, like biometrics and Radio Frequency Identification (RFID), more importantly when there is a pandemic that majorly spreads via touch. This clearly presents an opportunity in the field of facial feature detection and face recognition. We propose an effective and modish solution to mark attendance using the face recognition technique including Haar Cascade and Local Binary Pattern Histogram algorithms. The system will recognize the face of an individual or multiple students and compare them with the predefined face encoding to make a CSV file of attendees with their details. To create the database we will use image augmentation techniques. This system can also be used to tackle the problem of fake attendance and proxies.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114659973","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 : 2021-11-01DOI: 10.1109/ITSS-IoE53029.2021.9615301
Taha H. Rassem, Fatimah A. Alkareem, Mohammed Falah Mohammed, Nasrin M. Makbol, A. Sallam
To overcome noise sensitivity and increase the discriminative quality of the Local Binary Pattern, a Completed Local Ternary Count (CLTC) was developed by combining the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) (LBP). Furthermore, by integrating the proposed CLTC with the Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count, the proposed CLTC’s discriminative property is improved (WCLTC). As a result, more accurate local texture feature capture inside the RDWT domain is possible. The proposed WCLTC is utilised to perform texture and medical image classification tasks. The WCLTC performance is evaluated using two benchmark texture datasets, CUReT and Outex, as well as three medical picture databases, 2D Hela, VIRUS Texture, and BR datasets. With several of these datasets, the experimental findings demonstrate a remarkable classification accuracy. In several cases, the WCLTC performance outperformed the prior descriptions. With the 2D Hela, CUReT, and Virus datasets, the WCLTC achieves the highest classification accuracy of 96.91%, 97.04 percent, and 72.89%, respectively.
{"title":"A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification","authors":"Taha H. Rassem, Fatimah A. Alkareem, Mohammed Falah Mohammed, Nasrin M. Makbol, A. Sallam","doi":"10.1109/ITSS-IoE53029.2021.9615301","DOIUrl":"https://doi.org/10.1109/ITSS-IoE53029.2021.9615301","url":null,"abstract":"To overcome noise sensitivity and increase the discriminative quality of the Local Binary Pattern, a Completed Local Ternary Count (CLTC) was developed by combining the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) (LBP). Furthermore, by integrating the proposed CLTC with the Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count, the proposed CLTC’s discriminative property is improved (WCLTC). As a result, more accurate local texture feature capture inside the RDWT domain is possible. The proposed WCLTC is utilised to perform texture and medical image classification tasks. The WCLTC performance is evaluated using two benchmark texture datasets, CUReT and Outex, as well as three medical picture databases, 2D Hela, VIRUS Texture, and BR datasets. With several of these datasets, the experimental findings demonstrate a remarkable classification accuracy. In several cases, the WCLTC performance outperformed the prior descriptions. With the 2D Hela, CUReT, and Virus datasets, the WCLTC achieves the highest classification accuracy of 96.91%, 97.04 percent, and 72.89%, respectively.","PeriodicalId":230566,"journal":{"name":"2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127273207","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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