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: 10.1109/3ICT53449.2021.9581464
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: 10.1109/3ICT53449.2021.9581896
Muammer Catak, Sarah AlRasheedi, Norah AlAli, Ghadeer AlQallaf, Malak AlMeri, Bibi Ali
In this study, classical music has been investigated mainly based on pieces of well-known composers Mozart and Beethoven, then AI composer based on Markov chains and RNN has been proposed. AI is an efficient tool in science and technology for many specific applications including music field. The database has been collected based on 25 classical music sheets. The notes were separated in two groups where they are right hand and left hand. The database includes the notes and their frequencies and durations. The transition probability of each note were calculated. After the selection of the first note randomly, then the following notes were generated by means of the transition matrix. According to the results, both methods show an adequate level of quality considering the generation of notes by means of AI composer. The authors recommend to use Markov chains if a simple but efficient tool is appropriate considering the design criteria.
{"title":"Artificial Intelligence Composer","authors":"Muammer Catak, Sarah AlRasheedi, Norah AlAli, Ghadeer AlQallaf, Malak AlMeri, Bibi Ali","doi":"10.1109/3ICT53449.2021.9581896","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581896","url":null,"abstract":"In this study, classical music has been investigated mainly based on pieces of well-known composers Mozart and Beethoven, then AI composer based on Markov chains and RNN has been proposed. AI is an efficient tool in science and technology for many specific applications including music field. The database has been collected based on 25 classical music sheets. The notes were separated in two groups where they are right hand and left hand. The database includes the notes and their frequencies and durations. The transition probability of each note were calculated. After the selection of the first note randomly, then the following notes were generated by means of the transition matrix. According to the results, both methods show an adequate level of quality considering the generation of notes by means of AI composer. The authors recommend to use Markov chains if a simple but efficient tool is appropriate considering the design criteria.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"60 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":"124556920","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: 10.1109/3ICT53449.2021.9581693
Anwaar Buzaboon, Hanan Alboflasa, W. Alnaser, S. Shatnawi, Khawla Albinali
To evaluate the ESHERSs and determine their efficiency to measure environmental sustainability, we tackle this problem as a classification assignment. This study benchmark three ESHERSs: UI GreenMetric, Times Higher Education Impact ranking, and STARS (Sustainability Tracking, Assessment Rating System) by AASHE (the association for the advancement of sustainability in higher education). Next, we recruited a group of experts who mapped the ESHERS indicators to the SDGs indicators. Then, we use NLP techniques to classify (map) the ESHERS indicators to the SDGs indicators. Since most of the ESHERS indicators and the SDGs indicators are in the form of short text, we use the query expansion technique to make the NLP techniques more effective. Each ESHERS indicator and its expanded text represents a document. And, each SDG indicator and its expanded text represents a document. We took the expanded text from the description of the ESHERS indicators and the description of SDG indicators, forming the corpus for our study. Then, we used document similarity to find the similarity between every pair of the corpus documents. We used different similarity measures to see the similarity between the forms. Then, we used a voting system to map the ESHERSs indicators to the SDGs indicators. The proposed system was able to automatically map the underlying ranking systems indicators to the UN SDGs with 99% accuracy compared to the experts mapping.
为了评估eshers并确定其衡量环境可持续性的效率，我们将此问题作为分类分配来处理。本研究对三个eshers进行了基准测试:UI GreenMetric, Times Higher Education Impact排名，以及AASHE(高等教育可持续发展促进协会)的STARS(可持续性跟踪评估评级系统)。接下来，我们招募了一组专家，将ESHERS指标与可持续发展目标指标相对应。然后，我们使用自然语言处理技术将ESHERS指标分类(映射)到可持续发展目标指标。由于ESHERS指标和SDGs指标大多采用短文本的形式，我们使用查询扩展技术使NLP技术更加有效。每个ESHERS指标及其扩展文本代表一份文件。而且，每个可持续发展目标指标及其扩展文本代表一份文件。我们从ESHERS指标的描述和SDG指标的描述中提取了扩展文本，形成了我们研究的语料库。然后，我们使用文档相似度来寻找每对语料库文档之间的相似度。我们用不同的相似度来衡量表单之间的相似度。然后，我们使用投票系统将eshers指标映射到可持续发展目标指标。与专家绘制的地图相比，拟议的系统能够自动将基础排名系统指标映射到联合国可持续发展目标，准确率达到99%。
{"title":"Automated Mapping of Environmental Higher Education Ranking Systems Indicators to SDGs Indicators using Natural Language Processing and Document Similarity","authors":"Anwaar Buzaboon, Hanan Alboflasa, W. Alnaser, S. Shatnawi, Khawla Albinali","doi":"10.1109/3ICT53449.2021.9581693","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581693","url":null,"abstract":"To evaluate the ESHERSs and determine their efficiency to measure environmental sustainability, we tackle this problem as a classification assignment. This study benchmark three ESHERSs: UI GreenMetric, Times Higher Education Impact ranking, and STARS (Sustainability Tracking, Assessment Rating System) by AASHE (the association for the advancement of sustainability in higher education). Next, we recruited a group of experts who mapped the ESHERS indicators to the SDGs indicators. Then, we use NLP techniques to classify (map) the ESHERS indicators to the SDGs indicators. Since most of the ESHERS indicators and the SDGs indicators are in the form of short text, we use the query expansion technique to make the NLP techniques more effective. Each ESHERS indicator and its expanded text represents a document. And, each SDG indicator and its expanded text represents a document. We took the expanded text from the description of the ESHERS indicators and the description of SDG indicators, forming the corpus for our study. Then, we used document similarity to find the similarity between every pair of the corpus documents. We used different similarity measures to see the similarity between the forms. Then, we used a voting system to map the ESHERSs indicators to the SDGs indicators. The proposed system was able to automatically map the underlying ranking systems indicators to the UN SDGs with 99% accuracy compared to the experts mapping.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124585624","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: 10.1109/3ICT53449.2021.9582021
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: 10.1109/3ICT53449.2021.9581449
A. Mittal, H. Bhandari
There are very few studies that directly address the effects of technology adoption intention on the success of women entrepreneurs specifically in the Indian context. The current study addresses the linkage between technology adoption intention and its antecedents on the success of a very niche and unexplored segment of women entrepreneurs i.e., architects. Using a modified form of the unified theory of acceptance and use of technology (UTAUT) model, this study uses structural equation modeling to test the proposed model. The model consists of the following constructs: Mental Access towards technology, Technical Skills, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Technology Adoption Intention, and Women Entrepreneurial Success. The data has been collected from 188 respondents using the chain referral sampling method. The benefit of this study can be seen as a better understanding of technology adoption which will help to reduce barriers that women architects face in technology adoption and devise strategies promoting entrepreneurial success for women architects working all over India.
{"title":"Technology Adoption Intention as a Driver of Success of Women Architect Entrepreneurs","authors":"A. Mittal, H. Bhandari","doi":"10.1109/3ICT53449.2021.9581449","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581449","url":null,"abstract":"There are very few studies that directly address the effects of technology adoption intention on the success of women entrepreneurs specifically in the Indian context. The current study addresses the linkage between technology adoption intention and its antecedents on the success of a very niche and unexplored segment of women entrepreneurs i.e., architects. Using a modified form of the unified theory of acceptance and use of technology (UTAUT) model, this study uses structural equation modeling to test the proposed model. The model consists of the following constructs: Mental Access towards technology, Technical Skills, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Technology Adoption Intention, and Women Entrepreneurial Success. The data has been collected from 188 respondents using the chain referral sampling method. The benefit of this study can be seen as a better understanding of technology adoption which will help to reduce barriers that women architects face in technology adoption and devise strategies promoting entrepreneurial success for women architects working all over India.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"77 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":"122242116","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: 10.1109/3ICT53449.2021.9581504
Alba Muñoz Del Río, Isaac Segovia Ramírez, F. Márquez
Photovoltaic solar energy requires novel algorithms to ensure suitable maintenance management. Supervisory control and data acquisition system, combined with machine learning techniques, is required to obtain reliable information about the real state of photovoltaic systems. This paper introduces an Internet of Things platform for photovoltaic maintenance management based on classification algorithms to detect patterns, where performance ratio decreases significantly in time series. A real case study is presented with SCADA data from a photovoltaic solar plant located in Spain. The classification algorithms employed are Shapelets and K-nearest neighbors. The results prove the robust performance of both algorithms in pattern recognition, whereas K-nearest neighbors is preferable for implementation on the Internet of Things platform due to the reduced execution time. The application of the platform developed in this paper improve photovoltaic maintenance management detecting performance ratio reductions.
{"title":"Photovoltaic Solar Power Plant Maintenance Management based on IoT and Machine Learning","authors":"Alba Muñoz Del Río, Isaac Segovia Ramírez, F. Márquez","doi":"10.1109/3ICT53449.2021.9581504","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581504","url":null,"abstract":"Photovoltaic solar energy requires novel algorithms to ensure suitable maintenance management. Supervisory control and data acquisition system, combined with machine learning techniques, is required to obtain reliable information about the real state of photovoltaic systems. This paper introduces an Internet of Things platform for photovoltaic maintenance management based on classification algorithms to detect patterns, where performance ratio decreases significantly in time series. A real case study is presented with SCADA data from a photovoltaic solar plant located in Spain. The classification algorithms employed are Shapelets and K-nearest neighbors. The results prove the robust performance of both algorithms in pattern recognition, whereas K-nearest neighbors is preferable for implementation on the Internet of Things platform due to the reduced execution time. The application of the platform developed in this paper improve photovoltaic maintenance management detecting performance ratio reductions.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"27 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":"124822386","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: 10.1109/3ICT53449.2021.9581558
F. Harrou, Nabil Zerrouki, Abdelkader Dairi, Ying Sun, A. Houacine
Accurately detecting human falls of elderly people at an early stage is vital for providing early alert and avoid serious injury. Towards this purpose, multiple triaxial accelerometers data has been used to uncover falls based on an unsupervised monitoring procedure. Specifically, this paper introduces a one-class support vector machine (OCSVM) scheme into human fall detection. The main motivation behind the use of OCSVM is that it is a distribution-free learning model and can separate nonlinear features in an unsupervised way need for labeled data. The proposed OCSVM scheme was evaluated on fall detection databases from the University of Rzeszow's. Three other promising classification algorithms, Mean shift, Expectation-Maximization, k-means, were also assessed based on the same datasets. Their detection performances were compared with those obtained by the OCSVM algorithm. The results showed that the OCSVM scheme outperformed the other methods.
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