Pub Date : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357731
Kritika Rana, P. Kaur
Autonomous vehicles utilize a large amount of data from Machine Learning, Neural networks, Image recognition systems for building the techniques that can drive autonomously. Autonomous vehicles depend on sensors for measuring conditions of roads and for making decisions while driving, and safety depends on the consistency of these sensors. Autonomous vehicles are robotic systems that are not only capable of regulating their motion in response to the sensory data they have obtained, but are also capable of behaving intelligently (or flexibly) in their environment. Autonomous vehicles must have the ability to see the things around it in order to know if they need to drive, to stop and turn, and handle the unexpected situations they come across. Each and every sensor has its own types of strengths and weaknesses in terms of range, recognition and reliability. Moreover, each sensor has its own advantages as well as disadvantages. This paper discusses the features of sensors used in autonomous vehicles and compares different set of sensors. We have used a Kalman filter for the detection and tracking of the car. We have used different parameters to see how tracking quality is affected by the tracker and also adjust the tracking filter to specify a different motion.
{"title":"Comparative study of Automotive Sensor technologies used for Unmanned Driving","authors":"Kritika Rana, P. Kaur","doi":"10.1109/iccakm50778.2021.9357731","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357731","url":null,"abstract":"Autonomous vehicles utilize a large amount of data from Machine Learning, Neural networks, Image recognition systems for building the techniques that can drive autonomously. Autonomous vehicles depend on sensors for measuring conditions of roads and for making decisions while driving, and safety depends on the consistency of these sensors. Autonomous vehicles are robotic systems that are not only capable of regulating their motion in response to the sensory data they have obtained, but are also capable of behaving intelligently (or flexibly) in their environment. Autonomous vehicles must have the ability to see the things around it in order to know if they need to drive, to stop and turn, and handle the unexpected situations they come across. Each and every sensor has its own types of strengths and weaknesses in terms of range, recognition and reliability. Moreover, each sensor has its own advantages as well as disadvantages. This paper discusses the features of sensors used in autonomous vehicles and compares different set of sensors. We have used a Kalman filter for the detection and tracking of the car. We have used different parameters to see how tracking quality is affected by the tracker and also adjust the tracking filter to specify a different motion.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122768860","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 : 2021-01-19DOI: 10.1109/ICCAKM50778.2021.9357769
O. A. Mohammad, M. Elhadef
This research detects and recognize the emotions in Arabic speech audio files that contains records of human voices with different emotion classes (sad, happy, surprised, and questioning). In the area of emotion detection, when a person becomes emotional, his voice is adjusted based on the state of emotion. As the acoustic features like pressure, strength and loudness varies from a state of emotion to another. However, in the detection of feelings, the classification and modeling part of the features gets priority with the extracted features. Therefore, extracting the best features that describes the emotions stats is the most challenging task. This paper proposes an efficient approach to recognize the Arabic speech emotions. The presented method contains three main phases, signal preprocessing phase for noise removal and signal bandwidth reduction, feature extraction phase using a combination of Linear Predictive Codes (LPC) and the 10-degree polynomial Curve fitting Coefficients over the periodogram power spectral density function of the speech signal and machine learning phase using various machine learning algorithms (ANN, KNN, SVM, Decision Tree, Logistic Regression) and compare between their accuracy results to get the best accuracy.
{"title":"Arabic Speech Emotion Recognition Method Based On LPC And PPSD","authors":"O. A. Mohammad, M. Elhadef","doi":"10.1109/ICCAKM50778.2021.9357769","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357769","url":null,"abstract":"This research detects and recognize the emotions in Arabic speech audio files that contains records of human voices with different emotion classes (sad, happy, surprised, and questioning). In the area of emotion detection, when a person becomes emotional, his voice is adjusted based on the state of emotion. As the acoustic features like pressure, strength and loudness varies from a state of emotion to another. However, in the detection of feelings, the classification and modeling part of the features gets priority with the extracted features. Therefore, extracting the best features that describes the emotions stats is the most challenging task. This paper proposes an efficient approach to recognize the Arabic speech emotions. The presented method contains three main phases, signal preprocessing phase for noise removal and signal bandwidth reduction, feature extraction phase using a combination of Linear Predictive Codes (LPC) and the 10-degree polynomial Curve fitting Coefficients over the periodogram power spectral density function of the speech signal and machine learning phase using various machine learning algorithms (ANN, KNN, SVM, Decision Tree, Logistic Regression) and compare between their accuracy results to get the best accuracy.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114413798","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357701
P. Tamilarasi, D. Akila
In Big Data Cloud atmosphere, the cloud service provider (CSP) offers amenities to the customer with the accessible virtual cloud sources. Investigators have been provided more consideration towards the harmonizing of the load, as it has a complete impact on the system act. In this paper, Prediction based Load Balancing and Virtual Machine (VM) Migration (PLBVM) algorithm is designed for Big data cloud environments. In this algorithm, the future loads of each server are estimated. If the estimated future load is greater than an upper bound or less than a lower bound, then it indicates unbalanced load, so that VM migration is triggered. In VM migration, the VMs with minimum migration time and sufficient resources are selected. Then the task execution continues in the migrated VMs. By experimental results, it is shown that PLBVM achieves lesser response delay and execution time, among the other approaches.
{"title":"Prediction based Load Balancing and VM Migration in Big Data Cloud Environment","authors":"P. Tamilarasi, D. Akila","doi":"10.1109/iccakm50778.2021.9357701","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357701","url":null,"abstract":"In Big Data Cloud atmosphere, the cloud service provider (CSP) offers amenities to the customer with the accessible virtual cloud sources. Investigators have been provided more consideration towards the harmonizing of the load, as it has a complete impact on the system act. In this paper, Prediction based Load Balancing and Virtual Machine (VM) Migration (PLBVM) algorithm is designed for Big data cloud environments. In this algorithm, the future loads of each server are estimated. If the estimated future load is greater than an upper bound or less than a lower bound, then it indicates unbalanced load, so that VM migration is triggered. In VM migration, the VMs with minimum migration time and sufficient resources are selected. Then the task execution continues in the migrated VMs. By experimental results, it is shown that PLBVM achieves lesser response delay and execution time, among the other approaches.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117076072","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357710
S. Jeyalaksshmi, M. S. Nidhya, G. Suseendran, Souvik Pal, D. Akila
While the placement of the Virtual Computer was It remains an open challenge for Volunteer Cloud Computing, which reveals many divergent gaps in conventional cloud computing contexts, and has been thoroughly studied. Features, including sporadic usability of nodes and Infrastructure that's inefficient. In this article, we are modeling the Virtual In Volunteer Cloud Computing, computer positioning dilemma As a multi-dimensional, constrained 0–1 knapsack issue and Built algorithms to satisfy the basic aims and shortcomings of Volunteer Cloud Computing. The proof of a dedicated Cloud Computing Volunteer, The competitive success outcomes of these test beds are illustrated With algorithm.
{"title":"Developing Mapping and allotment in Volunteer Cloud systems using Reliability Profile algorithms in a virtual machine","authors":"S. Jeyalaksshmi, M. S. Nidhya, G. Suseendran, Souvik Pal, D. Akila","doi":"10.1109/iccakm50778.2021.9357710","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357710","url":null,"abstract":"While the placement of the Virtual Computer was It remains an open challenge for Volunteer Cloud Computing, which reveals many divergent gaps in conventional cloud computing contexts, and has been thoroughly studied. Features, including sporadic usability of nodes and Infrastructure that's inefficient. In this article, we are modeling the Virtual In Volunteer Cloud Computing, computer positioning dilemma As a multi-dimensional, constrained 0–1 knapsack issue and Built algorithms to satisfy the basic aims and shortcomings of Volunteer Cloud Computing. The proof of a dedicated Cloud Computing Volunteer, The competitive success outcomes of these test beds are illustrated With algorithm.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114086711","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 : 2021-01-19DOI: 10.1109/ICCAKM50778.2021.9357709
C. Sudha, D. Akila
This paper proposes a Credit Card Fraud Detection system based on Operational & Transaction features using Support Vector Machine (SVM) and Random Forest (RF) classifiers. In this system, in the first phase, the operational features of users are extracted, and then a random forest classifier is used to classify the features into benign and suspected. In the second phase, the transaction features of users are extracted from the user records, and then the M-class SVM classifier is applied to classify the features into benign and suspected. The performance of the system is evaluated in terms of standard measures precision, accuracy, recall, and F-1 score. By results, it was shown that both RF and SVM classifiers achieve a higher detection rate with good accuracy.
{"title":"Credit Card Fraud Detection System based on Operational & Transaction features using SVM and Random Forest Classifiers","authors":"C. Sudha, D. Akila","doi":"10.1109/ICCAKM50778.2021.9357709","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357709","url":null,"abstract":"This paper proposes a Credit Card Fraud Detection system based on Operational & Transaction features using Support Vector Machine (SVM) and Random Forest (RF) classifiers. In this system, in the first phase, the operational features of users are extracted, and then a random forest classifier is used to classify the features into benign and suspected. In the second phase, the transaction features of users are extracted from the user records, and then the M-class SVM classifier is applied to classify the features into benign and suspected. The performance of the system is evaluated in terms of standard measures precision, accuracy, recall, and F-1 score. By results, it was shown that both RF and SVM classifiers achieve a higher detection rate with good accuracy.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114232266","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357737
{"title":"[Copyright notice]","authors":"","doi":"10.1109/iccakm50778.2021.9357737","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357737","url":null,"abstract":"","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133540046","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 : 2021-01-19DOI: 10.1109/ICCAKM50778.2021.9357741
C. Verma, Z. Illés, Veronika Stoffová, Viktoria Bakonvi
This paper used descriptive analysis and nonparametric test to explore the significant difference between Indian and Hungarian students' attitudes. A systematic data analysis was performed with primary samples (314) gathered from Indian and Hungarian universities. We proposed two significant hypotheses to achieve the main objective of the study. On the one hand, we did not find a significant difference for “Informative and quality based study” and “Confidence and motivation”, and another hand, both country students, have dissimilar thinking towards “Independent learning”, “Admission/job placement/examination”, “Future acceptance for 21st century” and “Rapid deliver and share content”. The descriptive analysis also proved that both country students have a positive attitude towards technology. Results of the paper also proved that Hungarian students think more positively as compared to Indian students. We proposed a comparative technique to be implemented as a real-time web module with the university website's integration. This method may overcome the traditional offline differential approaches.
{"title":"Towards Technology Attitude comparison of Hungarian and Indian Student for Real- Time","authors":"C. Verma, Z. Illés, Veronika Stoffová, Viktoria Bakonvi","doi":"10.1109/ICCAKM50778.2021.9357741","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357741","url":null,"abstract":"This paper used descriptive analysis and nonparametric test to explore the significant difference between Indian and Hungarian students' attitudes. A systematic data analysis was performed with primary samples (314) gathered from Indian and Hungarian universities. We proposed two significant hypotheses to achieve the main objective of the study. On the one hand, we did not find a significant difference for “Informative and quality based study” and “Confidence and motivation”, and another hand, both country students, have dissimilar thinking towards “Independent learning”, “Admission/job placement/examination”, “Future acceptance for 21st century” and “Rapid deliver and share content”. The descriptive analysis also proved that both country students have a positive attitude towards technology. Results of the paper also proved that Hungarian students think more positively as compared to Indian students. We proposed a comparative technique to be implemented as a real-time web module with the university website's integration. This method may overcome the traditional offline differential approaches.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115513731","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 : 2021-01-19DOI: 10.1109/ICCAKM50778.2021.9357735
P. Saranya, K. Umamaheswari, M. Sivaram, Chirag Jain, Debarpan Bagchi
Diabetic Mellitus is the most familiar disease around the globe. Long prevalence of diabetes causes several problems related to health. The most common issue is Diabetic Retinopathy (DR). Diabetic retinopathy is a situation in which the vessels inside the retina are vandalized, leaking harmful substances and fluids in the surrounding tissue resulting in hemorrhages, micro aneurysms in the eye and further into partial or complete vision loss. This disease if treated in the early stage can help to prevent vision loss, but since it takes time for diagnosis and there is a shortage of ophthalmologists' patients suffer vision loss even before diagnosis. Hence, early detection of DR may help in reducing the problem. Therefore, in this paper we investigate various approaches to understand the process of detecting Diabetic Retinopathy as accurately as possible and classifying them into different grades of treatable DR (NPDR) namely LO, L1 DR, L2 DR and Proliferate DR (PDR) using Deep Learning and Image Processing techniques also making some improvisations on the same to enhance the capability of other existing systems.
糖尿病是世界上最常见的疾病。糖尿病的长期流行导致了一些与健康有关的问题。最常见的问题是糖尿病视网膜病变(DR)。糖尿病性视网膜病变是指视网膜内的血管被破坏，有害物质和液体在周围组织中泄漏，导致出血，眼睛出现微动脉瘤，进而部分或完全丧失视力。如果在早期治疗这种疾病，可以帮助防止视力下降，但由于诊断需要时间，而且眼科医生短缺，患者甚至在诊断之前就患有视力下降。因此，早期发现DR可能有助于减少这个问题。因此，在本文中，我们研究了各种方法来了解尽可能准确地检测糖尿病视网膜病变的过程，并使用深度学习和图像处理技术将其分为不同级别的可治疗DR (NPDR)，即LO, L1 DR, L2 DR和Proliferate DR (PDR)，并在此基础上进行了一些改进，以增强其他现有系统的能力。
{"title":"Classification of Different Stages of Diabetic Retinopathy using Convolutional Neural Networks","authors":"P. Saranya, K. Umamaheswari, M. Sivaram, Chirag Jain, Debarpan Bagchi","doi":"10.1109/ICCAKM50778.2021.9357735","DOIUrl":"https://doi.org/10.1109/ICCAKM50778.2021.9357735","url":null,"abstract":"Diabetic Mellitus is the most familiar disease around the globe. Long prevalence of diabetes causes several problems related to health. The most common issue is Diabetic Retinopathy (DR). Diabetic retinopathy is a situation in which the vessels inside the retina are vandalized, leaking harmful substances and fluids in the surrounding tissue resulting in hemorrhages, micro aneurysms in the eye and further into partial or complete vision loss. This disease if treated in the early stage can help to prevent vision loss, but since it takes time for diagnosis and there is a shortage of ophthalmologists' patients suffer vision loss even before diagnosis. Hence, early detection of DR may help in reducing the problem. Therefore, in this paper we investigate various approaches to understand the process of detecting Diabetic Retinopathy as accurately as possible and classifying them into different grades of treatable DR (NPDR) namely LO, L1 DR, L2 DR and Proliferate DR (PDR) using Deep Learning and Image Processing techniques also making some improvisations on the same to enhance the capability of other existing systems.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116041301","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357704
Vighnesh Shenoy, K. Santhosh
A state observer estimates the state variables depending on the measurements of the output over a period for an observable system. Luenberger observers can be used when the sensor produces minimal noise. Whereas, for stochastic systems having measurement and process noise Kalman filters are more suitable. This paper reports a state observer model for a liquid level monitoring system using both Luenberger and Kalman methods. A CFD simulation is carried out to investigate the laminar type of water flow through an orifice meter with a definite pipe diameter, which aids in the calculation of pressure difference resulting in liquid level estimation.
{"title":"Design Of Estimator For Level Monitoring Using Data Driven Model","authors":"Vighnesh Shenoy, K. Santhosh","doi":"10.1109/iccakm50778.2021.9357704","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357704","url":null,"abstract":"A state observer estimates the state variables depending on the measurements of the output over a period for an observable system. Luenberger observers can be used when the sensor produces minimal noise. Whereas, for stochastic systems having measurement and process noise Kalman filters are more suitable. This paper reports a state observer model for a liquid level monitoring system using both Luenberger and Kalman methods. A CFD simulation is carried out to investigate the laminar type of water flow through an orifice meter with a definite pipe diameter, which aids in the calculation of pressure difference resulting in liquid level estimation.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117183186","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 : 2021-01-19DOI: 10.1109/iccakm50778.2021.9357740
L. P. Brasen, Torben Tambo
With Risk being an ever-present concern, the Danish Power Transmission Industry (DPT) is no stranger to the concept. However, with growing complexity in the asset portfolio and an organization that wants to conform with agile methods, the DPT involved in this paper, are facing challenges regarding risk management in asset management activities. Disregarded and minor risks are backlogged and surveyed only once a year and a strong desire to change that fact have arisen in the asset management department. The aim of this study is therefore to examine the literature and investigate the current processes at the DPT, to ensure the development of a solution that can encapsulate and solve the disregarded and minor risks.
{"title":"Complexity of Risk Management in Danish Power Transmission Industry: Managing Disregarded and Minor risks","authors":"L. P. Brasen, Torben Tambo","doi":"10.1109/iccakm50778.2021.9357740","DOIUrl":"https://doi.org/10.1109/iccakm50778.2021.9357740","url":null,"abstract":"With Risk being an ever-present concern, the Danish Power Transmission Industry (DPT) is no stranger to the concept. However, with growing complexity in the asset portfolio and an organization that wants to conform with agile methods, the DPT involved in this paper, are facing challenges regarding risk management in asset management activities. Disregarded and minor risks are backlogged and surveyed only once a year and a strong desire to change that fact have arisen in the asset management department. The aim of this study is therefore to examine the literature and investigate the current processes at the DPT, to ensure the development of a solution that can encapsulate and solve the disregarded and minor risks.","PeriodicalId":165854,"journal":{"name":"2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124745946","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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