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: 10.1109/3ICT53449.2021.9581464
E. Prakasa, D. Prajitno, A. Nur, Kukuh Aji Sulistyo, Ema Rachmawati
Corn yield improvement program aims to attain continuous national self-sufficiency. The program needs to be supported by the availability of food resources, including high-quality corn seeds. In corn seed production, grading is one of the factors that affect the quality of corn seeds. The grading process is conducted manually by visual observations of workers. This process tends to be subjective and ineffective. Some corn seed factories use sieve machines to do grading by seed size. In this paper, an imaging-based classification system is proposed to perform corn seeds (BIMA-20 URI Hybrid) grading of two classes, which are categorised as good and bad. Three different methods are studied in the paper. The methods are respectively based on (1) shape, colour, and size features, (2) seed roundness, and (3) deep learning approach. Images data is acquired in a group of five corn kernels. Region-of-interest (ROI) segmentation is performed to select every single seed from the group image. Features values are then extracted from a single seed image and used as a classification parameter. The F1score of the proposed classification system, roundness differentiation, and model training performance can be used to show the categorisation capability. The deep learning approach has achieved the best F1score among the other proposed techniques. The best F1value, 0.983, is obtained at the ResNet-50 implementation. In separated observation, Method 6 (Size and Colour), Method 7 (Size, Shape, and Colour), Roundness, and ResNet-50 are represented as the best model for each group method. These methods reach F1scores more than 0.9, except the roundness parameter. The F1score of the roundness parameter is found at 0.854. Additional parameters might be required by the method based on the roundness feature for improving its final performance.
玉米增产计划旨在实现国家持续的自给自足。该计划需要得到粮食资源的支持，包括优质玉米种子。在玉米种子生产中，分级是影响玉米种子品质的因素之一。分级过程是通过工人的目视观察手动进行的。这个过程往往是主观的和无效的。有些玉米种子厂用筛机按种子大小分级。本文提出了一种基于图像的玉米种子分类系统(BIMA-20 URI Hybrid)，将玉米种子分为好、坏两类。本文研究了三种不同的方法。这些方法分别基于(1)形状、颜色和大小特征，(2)种子圆度，(3)深度学习方法。图像数据以五粒玉米粒为一组获取。进行感兴趣区域(ROI)分割，从组图像中选择每一个种子。然后从单个种子图像中提取特征值并用作分类参数。本文提出的分类系统的f1分数、圆度区分和模型训练性能可以用来表示分类能力。深度学习方法在其他提出的技术中获得了最好的f1分数。在ResNet-50实现中获得了最佳的f1值0.983。在单独观察中，Method 6 (Size and color)、Method 7 (Size, Shape, and color)、Roundness和ResNet-50被表示为每组方法的最佳模型。除圆度参数外，其他方法的得分均在0.9以上。圆度参数的F1score为0.854。该方法可能需要基于圆度特征的附加参数以改善其最终性能。
{"title":"Quality Categorisation of Corn (Zea mays) Seed using Feature-Based Classifier and Deep Learning on Digital Images","authors":"E. Prakasa, D. Prajitno, A. Nur, Kukuh Aji Sulistyo, Ema Rachmawati","doi":"10.1109/3ICT53449.2021.9581464","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581464","url":null,"abstract":"Corn yield improvement program aims to attain continuous national self-sufficiency. The program needs to be supported by the availability of food resources, including high-quality corn seeds. In corn seed production, grading is one of the factors that affect the quality of corn seeds. The grading process is conducted manually by visual observations of workers. This process tends to be subjective and ineffective. Some corn seed factories use sieve machines to do grading by seed size. In this paper, an imaging-based classification system is proposed to perform corn seeds (BIMA-20 URI Hybrid) grading of two classes, which are categorised as good and bad. Three different methods are studied in the paper. The methods are respectively based on (1) shape, colour, and size features, (2) seed roundness, and (3) deep learning approach. Images data is acquired in a group of five corn kernels. Region-of-interest (ROI) segmentation is performed to select every single seed from the group image. Features values are then extracted from a single seed image and used as a classification parameter. The F1score of the proposed classification system, roundness differentiation, and model training performance can be used to show the categorisation capability. The deep learning approach has achieved the best F1score among the other proposed techniques. The best F1value, 0.983, is obtained at the ResNet-50 implementation. In separated observation, Method 6 (Size and Colour), Method 7 (Size, Shape, and Colour), Roundness, and ResNet-50 are represented as the best model for each group method. These methods reach F1scores more than 0.9, except the roundness parameter. The F1score of the roundness parameter is found at 0.854. Additional parameters might be required by the method based on the roundness feature for improving its final performance.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"95 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":"123018959","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: 10.1109/3ICT53449.2021.9581646
Abdulrahman Atwah, Amjed Al-mousa
Car accidents have always been a terrible and extremely dangerous phenomenon. It caused the loss of many lives. The delay of the needed medical treatment for injuries at accident locations puts lives at risk. In this work, machine learning was used to predict the severity of accidents that occurred in the United Kingdom between the years 2005 – 2014. The combination of this AI solution and other systems to report to relevant authorities when accidents occur will preserve more lives. The medical support that will reach the accident location will depend on the severity of the accident. Several machine learning models were used, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The best accuracy has been achieved was using the RF model with an accuracy of 83.9 %.
{"title":"Car Accident Severity Classification Using Machine Learning","authors":"Abdulrahman Atwah, Amjed Al-mousa","doi":"10.1109/3ICT53449.2021.9581646","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581646","url":null,"abstract":"Car accidents have always been a terrible and extremely dangerous phenomenon. It caused the loss of many lives. The delay of the needed medical treatment for injuries at accident locations puts lives at risk. In this work, machine learning was used to predict the severity of accidents that occurred in the United Kingdom between the years 2005 – 2014. The combination of this AI solution and other systems to report to relevant authorities when accidents occur will preserve more lives. The medical support that will reach the accident location will depend on the severity of the accident. Several machine learning models were used, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The best accuracy has been achieved was using the RF model with an accuracy of 83.9 %.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"13 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":"127844818","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.
{"title":"An Intensity Estimation Application Based on Website Microservice Logs","authors":"A. Müngen, Iclal Cetin Tas","doi":"10.1109/3ICT53449.2021.9582107","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582107","url":null,"abstract":"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.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"56 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":"117107895","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: 10.1109/3ICT53449.2021.9581384
Fadheela Hussain, M. Hammad, W. El-Medany, Riadh Ksantini
Heart disease patient's classification is one of the most important keys in cardiovascular disease diagnosis. Researchers used several data mining methods to support healthcare specialists in the disease's analysis. This research has studied diverse of supervised machine learning systems for heart disease data classification, Decision Tree (DT), Artificial Neural Networks (ANN) classifiers, Naïve Bayes (NB), and Support Vector Machine (SVM), and have been used over two datasets of heart disease archives from the UCI machine-learning source. Results showed that ANN, the networks that are motivated via biological neural networks classifier overtook the three other classifiers with highest accuracy rate. The remaining classifiers returned lower performance than ANN. Moreover, enhancement is essential as misclassification is costly, so further improvement is required.
{"title":"Cardiovascular Diseases Classification Via Machine Learning Systems","authors":"Fadheela Hussain, M. Hammad, W. El-Medany, Riadh Ksantini","doi":"10.1109/3ICT53449.2021.9581384","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581384","url":null,"abstract":"Heart disease patient's classification is one of the most important keys in cardiovascular disease diagnosis. Researchers used several data mining methods to support healthcare specialists in the disease's analysis. This research has studied diverse of supervised machine learning systems for heart disease data classification, Decision Tree (DT), Artificial Neural Networks (ANN) classifiers, Naïve Bayes (NB), and Support Vector Machine (SVM), and have been used over two datasets of heart disease archives from the UCI machine-learning source. Results showed that ANN, the networks that are motivated via biological neural networks classifier overtook the three other classifiers with highest accuracy rate. The remaining classifiers returned lower performance than ANN. Moreover, enhancement is essential as misclassification is costly, so further improvement is required.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"13 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":"121923238","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: 10.1109/3ICT53449.2021.9582021
A. Alalawi
Educational organizations have used e-learning as an alternative to traditional learning at the COVID-19 pandemic and the need for social distancing. This paper presents the e-learning methods used during the COVID-19 pandemic period in public Bahrain schools. In addition, determines the positive and negative effects of the e-learning system. This research was conducted using a sample of 522 students from different age groups and different schools to measure the level of e-learning performance. The study showed that most students believe the effectiveness of e-learning is high in providing academic requirements during the pandemic period. On the other hand, some obstacles affect the level of e-learning productivity, and plans must be developed to overcome the obstacles.
{"title":"A Survey on E-learning Methods and Effectiveness in Public Bahrain Schools during the COVID-19 pandemic","authors":"A. Alalawi","doi":"10.1109/3ICT53449.2021.9582021","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582021","url":null,"abstract":"Educational organizations have used e-learning as an alternative to traditional learning at the COVID-19 pandemic and the need for social distancing. This paper presents the e-learning methods used during the COVID-19 pandemic period in public Bahrain schools. In addition, determines the positive and negative effects of the e-learning system. This research was conducted using a sample of 522 students from different age groups and different schools to measure the level of e-learning performance. The study showed that most students believe the effectiveness of e-learning is high in providing academic requirements during the pandemic period. On the other hand, some obstacles affect the level of e-learning productivity, and plans must be developed to overcome the obstacles.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"18 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":"133960642","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: 10.1109/3ICT53449.2021.9581542
K. Ezzat, M. Elattar, O. Fahmy
The latest trend in multiple object tracking (MOT) is bending to utilize deep learning to improve tracking performance. With all advanced models such as R-CNN, YOLO, SSD, and RetinaNet, there will always be a time-accuracy trade-off which puts constraints to computer vision advancement. However, it is not trivial to solve those kinds of challenges using end-to-end deep learning models, adopting new strategies to enhance the aforementioned models are appreciated. In this paper we introduce a novel radon transformation based framework, which takes advantage of color space conversion and squeezes the MOT problem to signal domain using radon transformation. Afterwards, the inference of Minkowski distance between sequence of signals is used to estimate the objects' location. Adaptive Region of Interest (ROI) and thresholding criteria have been adopted to ensure the stability of the tracker. We experimentally demonstrated that the proposed method achieved a significant performance improvement in both The Multiple Object Tracking Accuracy (MOTA) and ID F1 (IDF1) with respect to previous state-of-the-art using two public benchmarks.
{"title":"MinkowRadon: Multi-Object Tracking Using Radon Transformation and Minkowski Distance","authors":"K. Ezzat, M. Elattar, O. Fahmy","doi":"10.1109/3ICT53449.2021.9581542","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581542","url":null,"abstract":"The latest trend in multiple object tracking (MOT) is bending to utilize deep learning to improve tracking performance. With all advanced models such as R-CNN, YOLO, SSD, and RetinaNet, there will always be a time-accuracy trade-off which puts constraints to computer vision advancement. However, it is not trivial to solve those kinds of challenges using end-to-end deep learning models, adopting new strategies to enhance the aforementioned models are appreciated. In this paper we introduce a novel radon transformation based framework, which takes advantage of color space conversion and squeezes the MOT problem to signal domain using radon transformation. Afterwards, the inference of Minkowski distance between sequence of signals is used to estimate the objects' location. Adaptive Region of Interest (ROI) and thresholding criteria have been adopted to ensure the stability of the tracker. We experimentally demonstrated that the proposed method achieved a significant performance improvement in both The Multiple Object Tracking Accuracy (MOTA) and ID F1 (IDF1) with respect to previous state-of-the-art using two public benchmarks.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"38 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":"131212848","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: 10.1109/3ICT53449.2021.9582114
A. Zeki, R. Taha, Sara Alshakrani
According to a World Health Organization (WHO) survey from 2018, diabetes mellitus is one of the rapidly developing chronic life-threatening illnesses, affecting 422 million people worldwide. Early diagnosis of diabetes is often preferred for a clinically significant outcome due to the occurrence of a long asymptomatic period. Data science approaches have the potential to help other research fields. The tools, which are heavily dependent on Data Mining (DM) techniques, can be used to forecast diabetes patients effectively. In this article, three DM methods are used to investigate the early detection of diabetes: Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF). According to this research study, the RF experiment results showed that it has the highest level of accuracy compared to other techniques.
{"title":"Developing A Predictive Model for Diabetes Using Data Mining Techniques","authors":"A. Zeki, R. Taha, Sara Alshakrani","doi":"10.1109/3ICT53449.2021.9582114","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582114","url":null,"abstract":"According to a World Health Organization (WHO) survey from 2018, diabetes mellitus is one of the rapidly developing chronic life-threatening illnesses, affecting 422 million people worldwide. Early diagnosis of diabetes is often preferred for a clinically significant outcome due to the occurrence of a long asymptomatic period. Data science approaches have the potential to help other research fields. The tools, which are heavily dependent on Data Mining (DM) techniques, can be used to forecast diabetes patients effectively. In this article, three DM methods are used to investigate the early detection of diabetes: Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF). According to this research study, the RF experiment results showed that it has the highest level of accuracy compared to other techniques.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"19 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":"115348930","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: 10.1109/3ICT53449.2021.9582081
Noor Dabeek, Rama Jaber, Yara Bsharat, Amjad Hawash, Baker K. Abdalhaq
With the continuous achievements in Information Technology and its applications in different life fields, huge amounts of data are generated daily that makes searching for specific data items is a time/effort consuming process. However, several techniques are implemented and used to seek information such as search engines and information generation centers. Requesting data from historical warehouses is a famous routine as well, since extracting knowledge from historical repositories is needed in several daily life applications. The Arabic language has a lot of historical repositories represented in literary periodicals and books. Prophet Mohammad's (PBUH) talks are one of these important historical sources that can be used for knowledge extraction. These talks are collected and verified by a set of Muslim scholars in which Al-Bukhari was a famous one of them. This work is related to visualize the narrators of prophet Mohammad's (PBUH) talks as an interactive graph for both the narrator's related information and the talks themselves. Moreover, a set of graph centrality measures have been executed in order to quantify the importance of each narrator in the process of talks narration. The conducted experimental test emerges the importance of using the Interactive Graph versus the manual searching of Ahadith.
{"title":"Visualizing Ruwah Related Data By Interactive Graph","authors":"Noor Dabeek, Rama Jaber, Yara Bsharat, Amjad Hawash, Baker K. Abdalhaq","doi":"10.1109/3ICT53449.2021.9582081","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582081","url":null,"abstract":"With the continuous achievements in Information Technology and its applications in different life fields, huge amounts of data are generated daily that makes searching for specific data items is a time/effort consuming process. However, several techniques are implemented and used to seek information such as search engines and information generation centers. Requesting data from historical warehouses is a famous routine as well, since extracting knowledge from historical repositories is needed in several daily life applications. The Arabic language has a lot of historical repositories represented in literary periodicals and books. Prophet Mohammad's (PBUH) talks are one of these important historical sources that can be used for knowledge extraction. These talks are collected and verified by a set of Muslim scholars in which Al-Bukhari was a famous one of them. This work is related to visualize the narrators of prophet Mohammad's (PBUH) talks as an interactive graph for both the narrator's related information and the talks themselves. Moreover, a set of graph centrality measures have been executed in order to quantify the importance of each narrator in the process of talks narration. The conducted experimental test emerges the importance of using the Interactive Graph versus the manual searching of Ahadith.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"16 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":"127863795","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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