Pub Date : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406548
Waleed M. Ismael, Mingsheng Gao, Ammar T. Zahary, Zaid Yemeni, Y. Ibrahim, Ammar Hawban
Nowadays, Internet of Things (IoT) has been widely employed in different applications, such as health care, manufacturing, and weather forecasting. However, due to sensor sensitivities, potential harsh environmental interference, and deception, IoT data is normally apt to be imperfect and erroneous. This paper presents an edge-based approach based on the Gaussian mixture model and fuzzy measure to detect anomalous data without prior knowledge or training to overcome such adverse issues. The experimental results demonstrate that the proposed approach is efficient and effective in detecting anomaly data and achieves detection accuracy ranging from 93% to 100%.
{"title":"Edge-based Anomaly Data Detection Approach for Wireless Sensor Network-based Internet of Things","authors":"Waleed M. Ismael, Mingsheng Gao, Ammar T. Zahary, Zaid Yemeni, Y. Ibrahim, Ammar Hawban","doi":"10.1109/ICTSA52017.2021.9406548","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406548","url":null,"abstract":"Nowadays, Internet of Things (IoT) has been widely employed in different applications, such as health care, manufacturing, and weather forecasting. However, due to sensor sensitivities, potential harsh environmental interference, and deception, IoT data is normally apt to be imperfect and erroneous. This paper presents an edge-based approach based on the Gaussian mixture model and fuzzy measure to detect anomalous data without prior knowledge or training to overcome such adverse issues. The experimental results demonstrate that the proposed approach is efficient and effective in detecting anomaly data and achieves detection accuracy ranging from 93% to 100%.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131292985","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406523
Nagmy A.A. Saleh, H. Ertunc, Radhwan A. A. Saleh, M. Rassam
A global health crisis is appeared due to the rapid transmission of the COVID-19 pandemic. According to the World Health Organization (WHO), one of the effective ways to decrease this transmission is wearing masks in crowded places. However, monitoring people by police is a weary and difficult process. Thanks to the improvement in technology and artificial intelligence that make task became easier. In this paper, a simple mask recognition model based on texture and color moments features is proposed. This model is deployed in two stages: first, texture and color moments features from the face image (31 features) are extracted using a hybridization between texture features and color moments features techniques. In order to extract the texture features, the image transformed into Gray Level Co-Occurrence Matrix (GLCM) then 22 statistical metrics were calculated. So as to extract the color moments features, the first, second and third moments have been calculated from each layer of the RGB image. Second, based on the extracted features, the images are classified using a Multi-Layer Perceptron model (MLP). The dataset used in this research consists of 1787 real images with masks and 1918 without masks. The obtained results showed that the accuracy achieved by the proposed model is 90.58% and the time complexity is 6.7379 seconds for training and 0.0023 seconds for prediction.
{"title":"A Simple Mask Detection Model Based On A Multi-Layer Perception Neural Network","authors":"Nagmy A.A. Saleh, H. Ertunc, Radhwan A. A. Saleh, M. Rassam","doi":"10.1109/ICTSA52017.2021.9406523","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406523","url":null,"abstract":"A global health crisis is appeared due to the rapid transmission of the COVID-19 pandemic. According to the World Health Organization (WHO), one of the effective ways to decrease this transmission is wearing masks in crowded places. However, monitoring people by police is a weary and difficult process. Thanks to the improvement in technology and artificial intelligence that make task became easier. In this paper, a simple mask recognition model based on texture and color moments features is proposed. This model is deployed in two stages: first, texture and color moments features from the face image (31 features) are extracted using a hybridization between texture features and color moments features techniques. In order to extract the texture features, the image transformed into Gray Level Co-Occurrence Matrix (GLCM) then 22 statistical metrics were calculated. So as to extract the color moments features, the first, second and third moments have been calculated from each layer of the RGB image. Second, based on the extracted features, the images are classified using a Multi-Layer Perceptron model (MLP). The dataset used in this research consists of 1787 real images with masks and 1918 without masks. The obtained results showed that the accuracy achieved by the proposed model is 90.58% and the time complexity is 6.7379 seconds for training and 0.0023 seconds for prediction.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"464 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115868849","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406520
Abdulfattah E. Ba Alawi, Amer Al-basser, A. Sallam, Amr Al-sabaeei, Hesham Al-khateeb
Tuberculosis disease has a big concern and it is spreading quickly across the world. The secret for managing the condition is an accurate diagnosis. Acid quick staining, conventional approaches such as tuberculin skin test (TST), yield findings are unreliable or require more time to detect. This paper presents an automated solution that uses chest radiographs to diagnose tuberculosis. Chest radiographic images are used for tuberculosis diagnosis. Tuberculosis in chest radiographs is difficult to investigate under the current system of cavity identification, ribs, and diaphragm removal. By using a CNN-based model, the lung area is separated to resolve the problems. The proposed technique can classify chest x-ray (CXR) images as Tuberculosis (TB) infected or not. We analyzed 3500 CXR cases and 3500 normal cases with exposure to tuberculosis. Then, we built and trained our own CNN and found that the features map or heat-map generated from this network performed a slightly better job. The implementation was done in Tensorflow and Keras library. An accuracy of 98.71%, a sensitivity of 98.86%, and a specificity of 98.57% were achieved.
{"title":"Convolutional Neural Networks Model for Screening Tuberculosis Disease","authors":"Abdulfattah E. Ba Alawi, Amer Al-basser, A. Sallam, Amr Al-sabaeei, Hesham Al-khateeb","doi":"10.1109/ICTSA52017.2021.9406520","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406520","url":null,"abstract":"Tuberculosis disease has a big concern and it is spreading quickly across the world. The secret for managing the condition is an accurate diagnosis. Acid quick staining, conventional approaches such as tuberculin skin test (TST), yield findings are unreliable or require more time to detect. This paper presents an automated solution that uses chest radiographs to diagnose tuberculosis. Chest radiographic images are used for tuberculosis diagnosis. Tuberculosis in chest radiographs is difficult to investigate under the current system of cavity identification, ribs, and diaphragm removal. By using a CNN-based model, the lung area is separated to resolve the problems. The proposed technique can classify chest x-ray (CXR) images as Tuberculosis (TB) infected or not. We analyzed 3500 CXR cases and 3500 normal cases with exposure to tuberculosis. Then, we built and trained our own CNN and found that the features map or heat-map generated from this network performed a slightly better job. The implementation was done in Tensorflow and Keras library. An accuracy of 98.71%, a sensitivity of 98.86%, and a specificity of 98.57% were achieved.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121627783","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406550
Amjad M. H. Aoun, A. A. Saeed, A. Gaid, Osama Y. A. Saeed, Mansour N. G. Mohammed, Burkan Hawash
The need for high data rates, and high spectral efficiencies has grown with the tremendous advances in wireless applications, services and technologies. However, the design of miniature antennas with low cost, wider bandwidth and narrow radiation pattern is required in order to meet that increased demands as well as to support the new 5G mm-Wave spectrum. This paper proposes a single band rectangular microstrip patch antenna with square slots loaded on the radiating patch and line feeding structure. Rogers RT/Duroid 5880 substrate material with a dielectric constant of 2.2, loss tangent of 0.0009 and height of 0.55 mm is used. The proposed design operates at the mm-Wave (E-band) suitable for 5G mobile backhaul applications. The antenna operates in the band ranging from 64.20 GHz to 77.45 GHz with the center frequency at 73.7793 GHz. This operating frequency band covers the majority portion of the E-band. The impedance bandwidth of the operating band is 13.25 GHz (17.95 % from the center frequency) with a realized return loss of 39.9187 dB and a maximum gain of 6.0865 dB at the center frequency. Compared with other studies, the proposed antenna has a compact structure of $5.8 times 7.3 times 0.55$ mm3. An important feature of the proposed antenna is that it has a low production cost, since it is printed on a cheap Rogers RT/Duroid 5880 substrate material. Therefore, the proposed antenna has the potential to meet all the necessary requirements to be used for E-band mobile backhaul applications.
{"title":"E-Band Slotted Microstrip Patch Antenna for 5G Mobile Backhaul Applications","authors":"Amjad M. H. Aoun, A. A. Saeed, A. Gaid, Osama Y. A. Saeed, Mansour N. G. Mohammed, Burkan Hawash","doi":"10.1109/ICTSA52017.2021.9406550","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406550","url":null,"abstract":"The need for high data rates, and high spectral efficiencies has grown with the tremendous advances in wireless applications, services and technologies. However, the design of miniature antennas with low cost, wider bandwidth and narrow radiation pattern is required in order to meet that increased demands as well as to support the new 5G mm-Wave spectrum. This paper proposes a single band rectangular microstrip patch antenna with square slots loaded on the radiating patch and line feeding structure. Rogers RT/Duroid 5880 substrate material with a dielectric constant of 2.2, loss tangent of 0.0009 and height of 0.55 mm is used. The proposed design operates at the mm-Wave (E-band) suitable for 5G mobile backhaul applications. The antenna operates in the band ranging from 64.20 GHz to 77.45 GHz with the center frequency at 73.7793 GHz. This operating frequency band covers the majority portion of the E-band. The impedance bandwidth of the operating band is 13.25 GHz (17.95 % from the center frequency) with a realized return loss of 39.9187 dB and a maximum gain of 6.0865 dB at the center frequency. Compared with other studies, the proposed antenna has a compact structure of $5.8 times 7.3 times 0.55$ mm3. An important feature of the proposed antenna is that it has a low production cost, since it is printed on a cheap Rogers RT/Duroid 5880 substrate material. Therefore, the proposed antenna has the potential to meet all the necessary requirements to be used for E-band mobile backhaul applications.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114181866","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406525
Mohammad Mubarrak Mohd Yusof, Ilyani Abd Khalid
Precision education adopted the principles of precision medicine. It applies algorithm, machine learning and data manipulation technique for prediction and ultimately will be referred to design individualized education intervention program. This study has reviewed various themes and applications in precision education research and subsequently design a prediction model by applying one of its technique which is deep learning in a field study conducted at Universiti Sultan Azlan Shah, Malaysia. The study is to predict student’s performance in an English course taken by Bachelor of Usuluddin and Bachelor of Syariah students. Folder classification testing of the proposed deep learning model, when it was run against 3 testing dataset, it gives 93% accuracy.
{"title":"Precision Education Reviews: A Case Study on Predicting Student’s Performance using Feed Forward Neural Network","authors":"Mohammad Mubarrak Mohd Yusof, Ilyani Abd Khalid","doi":"10.1109/ICTSA52017.2021.9406525","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406525","url":null,"abstract":"Precision education adopted the principles of precision medicine. It applies algorithm, machine learning and data manipulation technique for prediction and ultimately will be referred to design individualized education intervention program. This study has reviewed various themes and applications in precision education research and subsequently design a prediction model by applying one of its technique which is deep learning in a field study conducted at Universiti Sultan Azlan Shah, Malaysia. The study is to predict student’s performance in an English course taken by Bachelor of Usuluddin and Bachelor of Syariah students. Folder classification testing of the proposed deep learning model, when it was run against 3 testing dataset, it gives 93% accuracy.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122326788","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406547
Sarah Abdulmalek, Salah Al-Hagree, Muneer Alsurori, Mohammed Hadwan, Amal M. Aqlan, F. Alqasemi
Survey research is one of the important types of scientific research needed to address the advancement in specific research areas. In this paper, we aim to provide a critical review of name-matching algorithms related to Levenstein's algorithm used in Arabic and English languages. We have collected and reviewed 50 papers related to Levenstein's algorithm. Name matching algorithms are becoming increasingly important in areas such as text classification, proofreading, automatic correction, plagiarism detection, extracting and aggregating specific information from huge data. In addition to the retrieval and tracking topics as well as answering questions, recognizing handwriting, evaluating articles, and summarizing. Among the new application in the area of artificial intelligence is the conversation systems, which are programs that communicate directly with humans using different natural languages. The most important part of name-matching algorithms is finding similarities between words or terms. The application of natural language processing tasks in Arabic and English represents a very difficult process as they contain many characteristics. Nevertheless, several name-matching algorithms have been introduced in Arabic and English texts, the attention of this research is focused on reviewing the research studies related to Levenstein's algorithm besides listing down developed applications in different areas.
{"title":"Levenstein's Algorithm On English and Arabic: A Survey","authors":"Sarah Abdulmalek, Salah Al-Hagree, Muneer Alsurori, Mohammed Hadwan, Amal M. Aqlan, F. Alqasemi","doi":"10.1109/ICTSA52017.2021.9406547","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406547","url":null,"abstract":"Survey research is one of the important types of scientific research needed to address the advancement in specific research areas. In this paper, we aim to provide a critical review of name-matching algorithms related to Levenstein's algorithm used in Arabic and English languages. We have collected and reviewed 50 papers related to Levenstein's algorithm. Name matching algorithms are becoming increasingly important in areas such as text classification, proofreading, automatic correction, plagiarism detection, extracting and aggregating specific information from huge data. In addition to the retrieval and tracking topics as well as answering questions, recognizing handwriting, evaluating articles, and summarizing. Among the new application in the area of artificial intelligence is the conversation systems, which are programs that communicate directly with humans using different natural languages. The most important part of name-matching algorithms is finding similarities between words or terms. The application of natural language processing tasks in Arabic and English represents a very difficult process as they contain many characteristics. Nevertheless, several name-matching algorithms have been introduced in Arabic and English texts, the attention of this research is focused on reviewing the research studies related to Levenstein's algorithm besides listing down developed applications in different areas.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116337313","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406545
A. Sallam, Fawzi Al Qahtani, Abdulgoddoos S. A. Gaid
Internet of Things (IoT) devices are rapidly increasing in acceptance and proliferation such as smart home devices, wearable devices and industrial systems; all of those devices are connected simultaneously in centralized client/server model and requires to be authenticated through the server, for that the security issues become more challengeable. So, moving the IoT system into the decentralized path is the right solution to get rid of those security issues. One of the best common decentralization systems is a Blockchain (BC), which can address a lot of IoT issues, and as especially access control issues. In this paper, and through the processes of automated search and selection, this study obtains some studies publications in the field BC in IoT Recently published; and we conducted an extensive literature review to understand status of applying BC based solutions for IoT. In addition, we also highlighted issues pertaining to IoT, and displaying the studies contributions, and methodologies used to get rid of those issues pertaining to IoT by using BC features, this studies indicates through their Findings that the research in BC is becoming more prominent and requires more effort in developing new methodologies and framework to integrate BC.
{"title":"Blockchain in Internet of Things: a Systematic Literature Review","authors":"A. Sallam, Fawzi Al Qahtani, Abdulgoddoos S. A. Gaid","doi":"10.1109/ICTSA52017.2021.9406545","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406545","url":null,"abstract":"Internet of Things (IoT) devices are rapidly increasing in acceptance and proliferation such as smart home devices, wearable devices and industrial systems; all of those devices are connected simultaneously in centralized client/server model and requires to be authenticated through the server, for that the security issues become more challengeable. So, moving the IoT system into the decentralized path is the right solution to get rid of those security issues. One of the best common decentralization systems is a Blockchain (BC), which can address a lot of IoT issues, and as especially access control issues. In this paper, and through the processes of automated search and selection, this study obtains some studies publications in the field BC in IoT Recently published; and we conducted an extensive literature review to understand status of applying BC based solutions for IoT. In addition, we also highlighted issues pertaining to IoT, and displaying the studies contributions, and methodologies used to get rid of those issues pertaining to IoT by using BC features, this studies indicates through their Findings that the research in BC is becoming more prominent and requires more effort in developing new methodologies and framework to integrate BC.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116183841","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406541
Ghazi Alnowaini, Azmi Alttal, A. Alhaj
Different companies compete for control of the size of the commercial markets and to offer quality products. Companies are keen to examine the products and ensure that they are free of any defects or distortions before distributing them in markets in order to preserve the confidence of customers, but manual inspection is expensive and takes a lot of time. Investors tended to use modern technologies to implement the examination process. In this paper, an approach based on the association of robots with a computer vision system is proposed. A robot arm with 4 degrees of freedom is designed by SOLIDWORKS software that takes the cans into the conveyor belt then passes it to the examination room so that an image of the product is taken via camera attached to the computer and the image is processed by the LABVIEW program. The model was simulated using MATLAB, and Arduino microcontroller has been used for controlling the processes perform by the prototype. When a defective product passes a conveyor belt, the system changes the path to remove the product from the production line. The simulation and experimental results proved that the prototype is capable of grasping cans, then detecting the cans finally, taking the defective ones out of the production line. By using this technology in the product sorting process, the productivity will be increased and the quality will be enhanced in a short time. An accuracy of 96% has been accomplished by the proposed system, where of 50 samples only two samples haven’t detected.
{"title":"Design and simulation robotic arm with computer vision for inspection process","authors":"Ghazi Alnowaini, Azmi Alttal, A. Alhaj","doi":"10.1109/ICTSA52017.2021.9406541","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406541","url":null,"abstract":"Different companies compete for control of the size of the commercial markets and to offer quality products. Companies are keen to examine the products and ensure that they are free of any defects or distortions before distributing them in markets in order to preserve the confidence of customers, but manual inspection is expensive and takes a lot of time. Investors tended to use modern technologies to implement the examination process. In this paper, an approach based on the association of robots with a computer vision system is proposed. A robot arm with 4 degrees of freedom is designed by SOLIDWORKS software that takes the cans into the conveyor belt then passes it to the examination room so that an image of the product is taken via camera attached to the computer and the image is processed by the LABVIEW program. The model was simulated using MATLAB, and Arduino microcontroller has been used for controlling the processes perform by the prototype. When a defective product passes a conveyor belt, the system changes the path to remove the product from the production line. The simulation and experimental results proved that the prototype is capable of grasping cans, then detecting the cans finally, taking the defective ones out of the production line. By using this technology in the product sorting process, the productivity will be increased and the quality will be enhanced in a short time. An accuracy of 96% has been accomplished by the proposed system, where of 50 samples only two samples haven’t detected.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131079546","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406537
A. Al-hetar, Esmat A. M. Aqlan
Millimeter-wave (MMW) systems are finding various commercial applications with the increasingly growing demand for bandwidth. Microstrip patch antennas (MPA) have low-profile advantages and ease of processing. However, due to the geometry of the patch on a thick dielectric substrate, the performance (gain, bandwidth) of the antenna will be degraded. An engineered patch shape that directs the radiation can be designed with promising characteristics such as gain and bandwidth. The proposed shape of the patch antenna is presented in this paper. To characterize their performances, the numerical results (simulations using HFSS) for the designed antenna are presented. The proposed microstrip antenna resonates at 93 GHz with a realized gain of 8.2 dB and a bandwidth of 5 GHz. The results of the simulation show significant changes in the radiation pattern and bandwidth that can be used for Passive MMW Imaging purposes in the MAP array.
{"title":"High performance & Compact Size Of Microstrip Antenna For 5G applications","authors":"A. Al-hetar, Esmat A. M. Aqlan","doi":"10.1109/ICTSA52017.2021.9406537","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406537","url":null,"abstract":"Millimeter-wave (MMW) systems are finding various commercial applications with the increasingly growing demand for bandwidth. Microstrip patch antennas (MPA) have low-profile advantages and ease of processing. However, due to the geometry of the patch on a thick dielectric substrate, the performance (gain, bandwidth) of the antenna will be degraded. An engineered patch shape that directs the radiation can be designed with promising characteristics such as gain and bandwidth. The proposed shape of the patch antenna is presented in this paper. To characterize their performances, the numerical results (simulations using HFSS) for the designed antenna are presented. The proposed microstrip antenna resonates at 93 GHz with a realized gain of 8.2 dB and a bandwidth of 5 GHz. The results of the simulation show significant changes in the radiation pattern and bandwidth that can be used for Passive MMW Imaging purposes in the MAP array.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646528","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 : 2021-03-22DOI: 10.1109/ICTSA52017.2021.9406544
F. Alqasemi, Salah Al-Hagree, Amal M. Aqlan, Khaled M. Alalayah, Zahraa Almotwakl, Mohammed Hadwan
In recent years, Educational Data Mining (EDM) is a new field that has been employed for extracting intrinsic educational new facts. EDM has become a hot topic in the field of educational informatics. In this paper we had applied clustering analysis on Yemen regions education statistics. We had achieved a mining process using hierarchical algorithm. The clustering analysis depicts latent knowledge beneath education data, which is illustrated by a dendrogram; i.e. hierarchical diagram. By performing single-linkage method, we had categorized Yemen regions using education data analysis. This categorization is employed for generating hierarchical ranking, which draw general image of the implied knowledge of targeted domain. The results presents promising relations between Yemen regions, that would help decision makers to understand the nature of education variables, which are distributed over the country.
教育数据挖掘(Educational Data Mining, EDM)是近年来发展起来的一个新兴领域，主要用于提取教育领域内在的新事实。EDM已成为教育信息学领域的研究热点。本文将聚类分析应用于也门地区教育统计。我们使用分层算法实现了一个挖掘过程。聚类分析描述教育数据下的潜在知识，用树状图表示;即层次图。通过执行单链接方法，我们使用教育数据分析对也门地区进行了分类。利用这种分类方法生成层次排序，从而得到目标领域隐含知识的总体图像。研究结果显示了也门各地区之间有希望的关系，这将有助于决策者了解分布在全国各地的教育变量的性质。
{"title":"Education Data Mining For Yemen Regions Based On Hierarchical Clustering Analysis","authors":"F. Alqasemi, Salah Al-Hagree, Amal M. Aqlan, Khaled M. Alalayah, Zahraa Almotwakl, Mohammed Hadwan","doi":"10.1109/ICTSA52017.2021.9406544","DOIUrl":"https://doi.org/10.1109/ICTSA52017.2021.9406544","url":null,"abstract":"In recent years, Educational Data Mining (EDM) is a new field that has been employed for extracting intrinsic educational new facts. EDM has become a hot topic in the field of educational informatics. In this paper we had applied clustering analysis on Yemen regions education statistics. We had achieved a mining process using hierarchical algorithm. The clustering analysis depicts latent knowledge beneath education data, which is illustrated by a dendrogram; i.e. hierarchical diagram. By performing single-linkage method, we had categorized Yemen regions using education data analysis. This categorization is employed for generating hierarchical ranking, which draw general image of the implied knowledge of targeted domain. The results presents promising relations between Yemen regions, that would help decision makers to understand the nature of education variables, which are distributed over the country.","PeriodicalId":334654,"journal":{"name":"2021 International Conference of Technology, Science and Administration (ICTSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129018867","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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