Pub Date : 2020-12-01DOI: 10.1109/CSCI51800.2020.00277
George Wanganga, Yanzhen Qu
Traditional customer payment service scheduling approaches cannot cope with the modern demand for timely, high-quality service due to the disruption of big data within small and medium-sized payment solution providers (SaMS-PSP). While many customers have access to modern technologies to lodge their service requests easily and fast, SaMS-PSPs do not have equally automated big data-driven capabilities to handle the growing demands of these service requests. To effectively improve SaMS-PSP’s customer payment service requests processing speeds, personnel optimization, throughput, and low latency scheduling, we have developed a new customer payment service request scheduling algorithm via matching request priority with the best personnel to handle the request based on data analytics through machine learning. Our experiments and testing have confirmed the merits of this new algorithm. We are also in the process of applying this new algorithm in real-world payment operations.
{"title":"An Auto Optimized Payment Service Requests Scheduling Algorithm via Data Analytics through Machine Learning","authors":"George Wanganga, Yanzhen Qu","doi":"10.1109/CSCI51800.2020.00277","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00277","url":null,"abstract":"Traditional customer payment service scheduling approaches cannot cope with the modern demand for timely, high-quality service due to the disruption of big data within small and medium-sized payment solution providers (SaMS-PSP). While many customers have access to modern technologies to lodge their service requests easily and fast, SaMS-PSPs do not have equally automated big data-driven capabilities to handle the growing demands of these service requests. To effectively improve SaMS-PSP’s customer payment service requests processing speeds, personnel optimization, throughput, and low latency scheduling, we have developed a new customer payment service request scheduling algorithm via matching request priority with the best personnel to handle the request based on data analytics through machine learning. Our experiments and testing have confirmed the merits of this new algorithm. We are also in the process of applying this new algorithm in real-world payment operations.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122569463","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00222
Hojun Lee, Keeyeon Hwang, Minhee Kang, Jaein Song
Black ice is recognized as the main cause of major accidents in winter because it has characteristics that are difficult to identify with the naked eye. This is expected to be a potential cause of accidents in the era of automated vehicles as well. Accordingly, this study presents a CNN-based black ice detection plan to prevent traffic accidents caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, the data was augmented and the image of road environment in various environments was learned. Test results show that the proposed CNN model detected black ice with 96% accuracy and reproducibility(recall).
{"title":"Black ice detection using CNN for the Prevention of Accidents in Automated Vehicle","authors":"Hojun Lee, Keeyeon Hwang, Minhee Kang, Jaein Song","doi":"10.1109/CSCI51800.2020.00222","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00222","url":null,"abstract":"Black ice is recognized as the main cause of major accidents in winter because it has characteristics that are difficult to identify with the naked eye. This is expected to be a potential cause of accidents in the era of automated vehicles as well. Accordingly, this study presents a CNN-based black ice detection plan to prevent traffic accidents caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, the data was augmented and the image of road environment in various environments was learned. Test results show that the proposed CNN model detected black ice with 96% accuracy and reproducibility(recall).","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122590402","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00153
Atif Farid Mohammad, P. Bearse, I. R. I. Haque
This paper presents Healthcare Big Data Normalization using Computerized Provider Order Entry (CPOE) and application of Graph Theory. This is the process of entering physician orders directly into an electronic health record (EHR). CPOE replaces traditional pen and paper, email, fax, and telephone ordering methods. CPOE is an integral part of electronic medical records and a mandatory component for achieving Meaningful Use Stage 2 certification in health care. CPOE is vital because it helps reduce medical errors that can lead to morbidity and mortality and lowers health care costs. Relational databases are the most common type of database used in healthcare settings. The advantages of using a Relational Database Management System for CPOE are discussed, as well as the disadvantages. The Entity-Relationship diagram and schema for a medication CPOE system used in a small ambulatory medical clinic are provided. We also briefly discuss the potential use of a CPOE application and a NoSQL Open Source database, such as OrientDB, along with the benefits and challenges.
{"title":"Healthcare Big Data Normalization Graph Theory Implementation","authors":"Atif Farid Mohammad, P. Bearse, I. R. I. Haque","doi":"10.1109/CSCI51800.2020.00153","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00153","url":null,"abstract":"This paper presents Healthcare Big Data Normalization using Computerized Provider Order Entry (CPOE) and application of Graph Theory. This is the process of entering physician orders directly into an electronic health record (EHR). CPOE replaces traditional pen and paper, email, fax, and telephone ordering methods. CPOE is an integral part of electronic medical records and a mandatory component for achieving Meaningful Use Stage 2 certification in health care. CPOE is vital because it helps reduce medical errors that can lead to morbidity and mortality and lowers health care costs. Relational databases are the most common type of database used in healthcare settings. The advantages of using a Relational Database Management System for CPOE are discussed, as well as the disadvantages. The Entity-Relationship diagram and schema for a medication CPOE system used in a small ambulatory medical clinic are provided. We also briefly discuss the potential use of a CPOE application and a NoSQL Open Source database, such as OrientDB, along with the benefits and challenges.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114417353","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00124
Dena F. Mujtaba, N. Mahapatra
Hiring/recruitment is key to an organization’s ability to position itself for success by attracting the right talent. Similarly, job search enables workers to connect to the right jobs in the right organizations. To assist in the hiring and job search processes, many technology solutions such as interest inventories, job recommendation models, job boards, and career pathway planning tools have been developed. However, solutions for preparing job postings are lacking. Job postings/ads play an essential role in hiring the right talent since they signal to the jobseeker the knowledge, skills, abilities, and other occupation-related characteristics (KSAOs) needed for a job. If the job ad does not convey the correct occupational characteristics, it is less likely that a well-qualified candidate will apply. Therefore, we present an interactive job ad visualization tool that analyzes the text in a job ad and matches phrases in it to a large occupational taxonomy of KSAOs. We combine O*NET, an occupational taxonomy, with natural language processing to perform semantic similarity matching between KSAOs for an occupation and ad text, and thereby assist jobseekers in their search process and recruiters in preparing job ads.
{"title":"Mining and Analyzing Occupational Characteristics from Job Postings","authors":"Dena F. Mujtaba, N. Mahapatra","doi":"10.1109/CSCI51800.2020.00124","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00124","url":null,"abstract":"Hiring/recruitment is key to an organization’s ability to position itself for success by attracting the right talent. Similarly, job search enables workers to connect to the right jobs in the right organizations. To assist in the hiring and job search processes, many technology solutions such as interest inventories, job recommendation models, job boards, and career pathway planning tools have been developed. However, solutions for preparing job postings are lacking. Job postings/ads play an essential role in hiring the right talent since they signal to the jobseeker the knowledge, skills, abilities, and other occupation-related characteristics (KSAOs) needed for a job. If the job ad does not convey the correct occupational characteristics, it is less likely that a well-qualified candidate will apply. Therefore, we present an interactive job ad visualization tool that analyzes the text in a job ad and matches phrases in it to a large occupational taxonomy of KSAOs. We combine O*NET, an occupational taxonomy, with natural language processing to perform semantic similarity matching between KSAOs for an occupation and ad text, and thereby assist jobseekers in their search process and recruiters in preparing job ads.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122077219","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00031
Amr Attia, M. Faezipour, Abdel-shakour Abuzneid
This paper introduces an effective Network Intrusion Detection Systems (NIDS) framework that deploys incremental statistical damping features of the packets along with state-of- the-art machine/deep learning algorithms to detect malicious patterns. A comprehensive evaluation study is conducted between eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) where feature selection and/or feature dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are also integrated into the models to decrease the system complexity for achieving fast responses. Several experimental runs confirm how powerful machine/deep learning algorithms are for intrusion detection on known attacks when combined with the appropriate features extracted. To investigate unknown attacks, the models were trained on a subset of the attack datasets, while a different set (with a different attack type) was kept aside for testing. The decent results achieved further support the belief that through supervised learning, the model could additionally detect unknown attacks.
{"title":"Network Intrusion Detection with XGBoost and Deep Learning Algorithms: An Evaluation Study","authors":"Amr Attia, M. Faezipour, Abdel-shakour Abuzneid","doi":"10.1109/CSCI51800.2020.00031","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00031","url":null,"abstract":"This paper introduces an effective Network Intrusion Detection Systems (NIDS) framework that deploys incremental statistical damping features of the packets along with state-of- the-art machine/deep learning algorithms to detect malicious patterns. A comprehensive evaluation study is conducted between eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) where feature selection and/or feature dimensionality reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are also integrated into the models to decrease the system complexity for achieving fast responses. Several experimental runs confirm how powerful machine/deep learning algorithms are for intrusion detection on known attacks when combined with the appropriate features extracted. To investigate unknown attacks, the models were trained on a subset of the attack datasets, while a different set (with a different attack type) was kept aside for testing. The decent results achieved further support the belief that through supervised learning, the model could additionally detect unknown attacks.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128744966","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00301
O. Layode, M. Rahman
The COVID-19 pandemic is the defining global health crisis of our time which is currently challenging families, communities, health care systems, and government all over the world. It is critical to detect and isolate the positive cases as early as possible for timely treatment to prevent the further spread of the virus. It was found in few early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. In the current context, a rapid, accessible and automated screening tool based on image processing of chest X-rays (CXRs) would be much needed as a quick alternative to PCR testing, especially with commonly available X-ray machines and without the dedicated test kits in labs and hospitals. Several classifications based approaches have been proposed recently with encouraging results to detect pneumonia based on CXRs using supervised deep transfer learning techniques based on Convolutional Neural Networks (CNNs). These black box approaches are mainly non-interactive in nature and their prediction represents just a cue to the radiologist. This work focuses on issues related to the development of such an automated system for CXRs by performing discriminative feature learning using deep neural networks with a purely data driven approach and retrieving images based on an unknown query image and performing retrieval evaluation on currently available benchmark datasets towards the goal of realistic comparison and real clinical integration. The system is trained and tested on an image collection of 1700 CXRs obtained from two different resources with encouraging results based on precision and recall measures in individual deep feature spaces. It is hoped that the proposed system as diagnostic aid would reduce the visual observation error of human operators and enhance sensitivity in testing for Covid-19 detection.
{"title":"A Chest X-ray Image Retrieval System for COVID-19 Detection using Deep Transfer Learning and Denoising Auto Encoder","authors":"O. Layode, M. Rahman","doi":"10.1109/CSCI51800.2020.00301","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00301","url":null,"abstract":"The COVID-19 pandemic is the defining global health crisis of our time which is currently challenging families, communities, health care systems, and government all over the world. It is critical to detect and isolate the positive cases as early as possible for timely treatment to prevent the further spread of the virus. It was found in few early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. In the current context, a rapid, accessible and automated screening tool based on image processing of chest X-rays (CXRs) would be much needed as a quick alternative to PCR testing, especially with commonly available X-ray machines and without the dedicated test kits in labs and hospitals. Several classifications based approaches have been proposed recently with encouraging results to detect pneumonia based on CXRs using supervised deep transfer learning techniques based on Convolutional Neural Networks (CNNs). These black box approaches are mainly non-interactive in nature and their prediction represents just a cue to the radiologist. This work focuses on issues related to the development of such an automated system for CXRs by performing discriminative feature learning using deep neural networks with a purely data driven approach and retrieving images based on an unknown query image and performing retrieval evaluation on currently available benchmark datasets towards the goal of realistic comparison and real clinical integration. The system is trained and tested on an image collection of 1700 CXRs obtained from two different resources with encouraging results based on precision and recall measures in individual deep feature spaces. It is hoped that the proposed system as diagnostic aid would reduce the visual observation error of human operators and enhance sensitivity in testing for Covid-19 detection.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024278","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00108
Soheyla Amirian, Abolfazl Farahani, H. Arabnia, K. Rasheed, T. Taha
Video Captioning is considered to be one of the most challenging problems in the field of computer vision. Video Captioning involves the combination of different deep learning models to perform object detection, action detection, and localization by processing a sequence of image frames. It is crucial to consider the sequence of actions in a video in order to generate a meaningful description of the overall action event. A reliable, accurate, and real-time video captioning method can be used in many applications. However, this paper focuses on one application: video captioning for fostering and facilitating physical activities. In broad terms, the work can be considered to be assistive technology. Lack of physical activity appears to be increasingly widespread in many nations due to many factors, the most important being the convenience that technology has provided in workplaces. The adopted sedentary lifestyle is becoming a significant public health issue. Therefore, it is essential to incorporate more physical movements into our daily lives. Tracking one’s daily physical activities would offer a base for comparison with activities performed in subsequent days. With the above in mind, this paper proposes a video captioning framework that aims to describe the activities in a video and estimate a person’s daily physical activity level. This framework could potentially help people trace their daily movements to reduce an inactive lifestyle’s health risks. The work presented in this paper is still in its infancy. The initial steps of the application are outlined in this paper. Based on our preliminary research, this project has great merit.
{"title":"The Use of Video Captioning for Fostering Physical Activity","authors":"Soheyla Amirian, Abolfazl Farahani, H. Arabnia, K. Rasheed, T. Taha","doi":"10.1109/CSCI51800.2020.00108","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00108","url":null,"abstract":"Video Captioning is considered to be one of the most challenging problems in the field of computer vision. Video Captioning involves the combination of different deep learning models to perform object detection, action detection, and localization by processing a sequence of image frames. It is crucial to consider the sequence of actions in a video in order to generate a meaningful description of the overall action event. A reliable, accurate, and real-time video captioning method can be used in many applications. However, this paper focuses on one application: video captioning for fostering and facilitating physical activities. In broad terms, the work can be considered to be assistive technology. Lack of physical activity appears to be increasingly widespread in many nations due to many factors, the most important being the convenience that technology has provided in workplaces. The adopted sedentary lifestyle is becoming a significant public health issue. Therefore, it is essential to incorporate more physical movements into our daily lives. Tracking one’s daily physical activities would offer a base for comparison with activities performed in subsequent days. With the above in mind, this paper proposes a video captioning framework that aims to describe the activities in a video and estimate a person’s daily physical activity level. This framework could potentially help people trace their daily movements to reduce an inactive lifestyle’s health risks. The work presented in this paper is still in its infancy. The initial steps of the application are outlined in this paper. Based on our preliminary research, this project has great merit.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129170295","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00216
Carlos García-Mauriño, P. Zufiria, Alejandro Jarabo-Peñas
Statistical description and prediction of bus arrival times is relevant for public transport users since it allows more timewise efficient journeys. This work is focused on characterizing the real behavior of buses based on past arrival estimation data. The main goal is to estimate real bus pass times by optimally collecting data from an intercity bus arrival time estimation system which is limited in petition handling capacity. This requires to model the server behavior prior to the design of the data collection system. In addition, it also requires the design of an algorithm to estimate the bus real passing time considering that only the provided estimated time of arrival is available. This information can be useful for designing alternative online arrival time estimators based on supervised learning which could potentially improve the estimator efficiency.
{"title":"Bus Pass Time Estimation based on Efficient Data Gathering from a Slow Mobility Server","authors":"Carlos García-Mauriño, P. Zufiria, Alejandro Jarabo-Peñas","doi":"10.1109/CSCI51800.2020.00216","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00216","url":null,"abstract":"Statistical description and prediction of bus arrival times is relevant for public transport users since it allows more timewise efficient journeys. This work is focused on characterizing the real behavior of buses based on past arrival estimation data. The main goal is to estimate real bus pass times by optimally collecting data from an intercity bus arrival time estimation system which is limited in petition handling capacity. This requires to model the server behavior prior to the design of the data collection system. In addition, it also requires the design of an algorithm to estimate the bus real passing time considering that only the provided estimated time of arrival is available. This information can be useful for designing alternative online arrival time estimators based on supervised learning which could potentially improve the estimator efficiency.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130472563","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00258
Feng-Jen Yang
As a personal interest of study, I tried a logic programming approach towards the problem solving of cryptarithmetic puzzles that are commonly discussed as a subcategory of constraint satisfaction problems in the literature of artificial intelligence. While there are possibly several methods capable of solving constraint satisfaction problems, I took into consideration the efficiency as well as the completeness that will identify all possible solutions under the specified constraints and exclude trivial and useless solutions from the perspective of real-life practice. In this paper, I demonstrated an approach that can be adapted to solve most of the constraint satisfaction problems especially within the context of cryptarithmatic puzzles. This method will also perform forward checking to have early backtracking and prevent searching the entire search tree exhaustively.
{"title":"Solving Cryptarithmetic Puzzles by Logic Programming","authors":"Feng-Jen Yang","doi":"10.1109/CSCI51800.2020.00258","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00258","url":null,"abstract":"As a personal interest of study, I tried a logic programming approach towards the problem solving of cryptarithmetic puzzles that are commonly discussed as a subcategory of constraint satisfaction problems in the literature of artificial intelligence. While there are possibly several methods capable of solving constraint satisfaction problems, I took into consideration the efficiency as well as the completeness that will identify all possible solutions under the specified constraints and exclude trivial and useless solutions from the perspective of real-life practice. In this paper, I demonstrated an approach that can be adapted to solve most of the constraint satisfaction problems especially within the context of cryptarithmatic puzzles. This method will also perform forward checking to have early backtracking and prevent searching the entire search tree exhaustively.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123917631","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 : 2020-12-01DOI: 10.1109/CSCI51800.2020.00248
Chendi Cao, M. Neilsen
Modern seed breeding programs require the ability to analyze seeds efficiently to be useful. Even simple measures such as volume and density can be challenging to compute efficiently with modest equipment. Accurately measuring seed volume becomes a highly under-constrained problem. Multiple images from different perspectives are required.This paper presents an efficient and affordable 3D single seed volume measurement system to extract image contours and compute volumes using a modified volume carving method in a controlled lab environment. The framework is constructed with a turntable, a stepper motor controlled by an Arduino microcontroller, three orthogonal cameras, and camera control via a modest computer used for data acquisition and processing. For testing, images are captured using only a side camera from different angles by rotating the turntable. Then, the framework processes the multiple images in parallel and reconstructs 3D seed objects to calculate the volume based on the voxel numbers. The proposed framework: (1) generates single seed 3D geometries for visualization, (2) calculates precise seed volumes within seconds, and (3) achieves less than a 3% error rate on a reference ceramic sphere.
{"title":"Efficient Seed Volume Measurement Framework","authors":"Chendi Cao, M. Neilsen","doi":"10.1109/CSCI51800.2020.00248","DOIUrl":"https://doi.org/10.1109/CSCI51800.2020.00248","url":null,"abstract":"Modern seed breeding programs require the ability to analyze seeds efficiently to be useful. Even simple measures such as volume and density can be challenging to compute efficiently with modest equipment. Accurately measuring seed volume becomes a highly under-constrained problem. Multiple images from different perspectives are required.This paper presents an efficient and affordable 3D single seed volume measurement system to extract image contours and compute volumes using a modified volume carving method in a controlled lab environment. The framework is constructed with a turntable, a stepper motor controlled by an Arduino microcontroller, three orthogonal cameras, and camera control via a modest computer used for data acquisition and processing. For testing, images are captured using only a side camera from different angles by rotating the turntable. Then, the framework processes the multiple images in parallel and reconstructs 3D seed objects to calculate the volume based on the voxel numbers. The proposed framework: (1) generates single seed 3D geometries for visualization, (2) calculates precise seed volumes within seconds, and (3) achieves less than a 3% error rate on a reference ceramic sphere.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123599832","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：