Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099796
M. Mahyoub, Shatha Ghareeb, J. Mustafina
Home Loan plays a pivotal role in today's age when one steps into purchasing their home. It has been witnessed that in many cases users are unable to pay the after taking the loan and thus the loan is slipped to NPA(Non-Performing Asset) from Standard Asset for the bank or any lending institution. The revenue generation is ceased. As the housing loan is taken against property the lenders have right to sell the property and close the dues, but the process is lengthy as judicial procedures are involved. In most cases, the property value is much less than the calculated loan amount (Principal + Interest). In this study we examined the several ML methods to identify the loan default before disbursing the loan to the applicant. This matter has been studied widely and used the predictive analytics to find out the relationship between attributes and the target variable. Predictive Analytics enables us to feed optimal set of features to the ML models. The study started with 122 attributes and ended up with around 30% of features as the ideal subset for housing loan default prediction. Then, five ML models were fit into the dataset and the champion model came up with roc score 0.94, Recall 0.90 and Precision 0.94. LIME and SHAP were applied on the champion model along with the dataset for global and local interpretability. The experimental procedure concluded that ML models along with predictive analytics can arrest the loan disbursal to the ineligible applicants and will also provide the insight of such prediction with the help of model interpretability.
{"title":"A Novel Predictive Model for Housing Loan Default using Feature Generation and Explainable AI","authors":"M. Mahyoub, Shatha Ghareeb, J. Mustafina","doi":"10.1109/DeSE58274.2023.10099796","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099796","url":null,"abstract":"Home Loan plays a pivotal role in today's age when one steps into purchasing their home. It has been witnessed that in many cases users are unable to pay the after taking the loan and thus the loan is slipped to NPA(Non-Performing Asset) from Standard Asset for the bank or any lending institution. The revenue generation is ceased. As the housing loan is taken against property the lenders have right to sell the property and close the dues, but the process is lengthy as judicial procedures are involved. In most cases, the property value is much less than the calculated loan amount (Principal + Interest). In this study we examined the several ML methods to identify the loan default before disbursing the loan to the applicant. This matter has been studied widely and used the predictive analytics to find out the relationship between attributes and the target variable. Predictive Analytics enables us to feed optimal set of features to the ML models. The study started with 122 attributes and ended up with around 30% of features as the ideal subset for housing loan default prediction. Then, five ML models were fit into the dataset and the champion model came up with roc score 0.94, Recall 0.90 and Precision 0.94. LIME and SHAP were applied on the champion model along with the dataset for global and local interpretability. The experimental procedure concluded that ML models along with predictive analytics can arrest the loan disbursal to the ineligible applicants and will also provide the insight of such prediction with the help of model interpretability.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115243839","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099694
Mee Chun Loo, R. Logeswaran, Zailan Arabee bin Abdul Salam
Automated optical inspection (AOI) is a visual defect inspection system. The semiconductor industry has a strong dependency on AOI for defects screening. Conventional AOI is inadequate for some inspections, especially surface defects like crack, chip and void, and the algorithms are inefficient in isolating the defects from product variants. Convolutional Neural Network (CNN) had been broadly studied and adopted to replace the conventional AOI in surface inspection. There are many CNN architectures developed in the past decade for image classification, such as AlexNet, GoogLeNet, ResNet, VGGNet, etc.; each with its own strength in terms of accuracy and speed. The training process could be speeded up too using techniques such as transfer learning from pre-trained CNN models. Newer techniques in vector programming on kernels, e.g., Single Instruction Multiple Data (SIMD) and depth wise separable method can further increase the efficiency of convolutional layer activation functions. CNN algorithms for surface inspection are found to be very promising, with defect classification able to achieve accuracies of 91-99% on the wide range of products. The CNN result outperforms conventional surface inspection methods like edge detection and machine learning algorithms.
{"title":"CNN Aided Surface Inspection for SMT Manufacturing","authors":"Mee Chun Loo, R. Logeswaran, Zailan Arabee bin Abdul Salam","doi":"10.1109/DeSE58274.2023.10099694","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099694","url":null,"abstract":"Automated optical inspection (AOI) is a visual defect inspection system. The semiconductor industry has a strong dependency on AOI for defects screening. Conventional AOI is inadequate for some inspections, especially surface defects like crack, chip and void, and the algorithms are inefficient in isolating the defects from product variants. Convolutional Neural Network (CNN) had been broadly studied and adopted to replace the conventional AOI in surface inspection. There are many CNN architectures developed in the past decade for image classification, such as AlexNet, GoogLeNet, ResNet, VGGNet, etc.; each with its own strength in terms of accuracy and speed. The training process could be speeded up too using techniques such as transfer learning from pre-trained CNN models. Newer techniques in vector programming on kernels, e.g., Single Instruction Multiple Data (SIMD) and depth wise separable method can further increase the efficiency of convolutional layer activation functions. CNN algorithms for surface inspection are found to be very promising, with defect classification able to achieve accuracies of 91-99% on the wide range of products. The CNN result outperforms conventional surface inspection methods like edge detection and machine learning algorithms.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115249592","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099681
I. Makarova, G. Yakupova, P. Buyvol, E. Mukhametdinov, A. Abashev, J. Mustafina
When managing the transport system of an urbanized area, infrastructural changes cannot always solve transport problems. At the same time, organizational measures implemented within the framework of an intelligent transport system can be effective. To make operational and strategic decisions, it is necessary to form a base of typical emergency situations, having previously studied them on a simulation model. For this, we have chosen a micro-simulation method, which allows taking into account the stochastic nature of the traffic flow. As a result of a computer experiment, we have obtained estimates of changes in parameters (average time for a vehicle to cross an intersection in all directions, average speed) when emergencies of a given duration occur at a T-shaped intersection. The novelty of the proposed approach lies in the possibility of assessing the nature of the emergency situations' development for various values of influencing factors.
{"title":"Using Simulation for Investigating Emergency Traffic Situations","authors":"I. Makarova, G. Yakupova, P. Buyvol, E. Mukhametdinov, A. Abashev, J. Mustafina","doi":"10.1109/DeSE58274.2023.10099681","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099681","url":null,"abstract":"When managing the transport system of an urbanized area, infrastructural changes cannot always solve transport problems. At the same time, organizational measures implemented within the framework of an intelligent transport system can be effective. To make operational and strategic decisions, it is necessary to form a base of typical emergency situations, having previously studied them on a simulation model. For this, we have chosen a micro-simulation method, which allows taking into account the stochastic nature of the traffic flow. As a result of a computer experiment, we have obtained estimates of changes in parameters (average time for a vehicle to cross an intersection in all directions, average speed) when emergencies of a given duration occur at a T-shaped intersection. The novelty of the proposed approach lies in the possibility of assessing the nature of the emergency situations' development for various values of influencing factors.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114291525","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100308
Zoe Lim Mei Yi, Julia Juremi
The traditional way of taking attendance has been said to be inefficient and had to take a longer time to mark every attendee's presence. With that in mind, the research aims to eliminate the issue brought by the manual attendance system by developing a web-based attendance system that can record attendance in a faster and more effective way with the implementation of a face recognition system. The attendance will be taken with just one scan of the face of the attendees, ensuring their presence at the event. Not only that but also with the implementation of two-factor authentication (2FA) to develop a secure web-based system, as well as to protect users against cyber-attack. This system not only solved the problems brought by the current attendance system but also protects the environment by eliminating the need of using paper to record attendance.
{"title":"Dezvent - Digitalizing Attendance System with 2FA and Face Recognition Implementation","authors":"Zoe Lim Mei Yi, Julia Juremi","doi":"10.1109/DeSE58274.2023.10100308","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100308","url":null,"abstract":"The traditional way of taking attendance has been said to be inefficient and had to take a longer time to mark every attendee's presence. With that in mind, the research aims to eliminate the issue brought by the manual attendance system by developing a web-based attendance system that can record attendance in a faster and more effective way with the implementation of a face recognition system. The attendance will be taken with just one scan of the face of the attendees, ensuring their presence at the event. Not only that but also with the implementation of two-factor authentication (2FA) to develop a secure web-based system, as well as to protect users against cyber-attack. This system not only solved the problems brought by the current attendance system but also protects the environment by eliminating the need of using paper to record attendance.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129887737","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100055
M. Mahyoub, F. Natalia, S. Sudirman, J. Mustafina
Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others.
{"title":"Sign Language Recognition using Deep Learning","authors":"M. Mahyoub, F. Natalia, S. Sudirman, J. Mustafina","doi":"10.1109/DeSE58274.2023.10100055","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100055","url":null,"abstract":"Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123819804","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100283
D. M. Vistro, A. Rehman, Zufishan Hameed
Cloud computing becoming popular now day as, the world is moving towards vitalization and it provide resource to the users depending on their needs by using different resource allocation technique. Resilience and reliability is one of the major issue while dealing with cloud computing. Mitigation failure and migration failure are the issues in cloud resilience and reliability services which cause many service level objective violation. Many work have been done to improve the quality of resilience and reliability. The aim of this paper is to provide a better technique to avoid and recover from mitigation failure and a reliable resource allocation approach at minimum possible cost, for this purpose we used Cascading Failure Resilience System (CSFR) technique. Comparative analyses done to validate our approach and the results shows that our approach handle mitigation failure in an efficient way as well as it provide reliability while providing resources to the users at a comparative low cost.
{"title":"An Efficient Approach for Resilience and Reliability Against Cascading Failure","authors":"D. M. Vistro, A. Rehman, Zufishan Hameed","doi":"10.1109/DeSE58274.2023.10100283","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100283","url":null,"abstract":"Cloud computing becoming popular now day as, the world is moving towards vitalization and it provide resource to the users depending on their needs by using different resource allocation technique. Resilience and reliability is one of the major issue while dealing with cloud computing. Mitigation failure and migration failure are the issues in cloud resilience and reliability services which cause many service level objective violation. Many work have been done to improve the quality of resilience and reliability. The aim of this paper is to provide a better technique to avoid and recover from mitigation failure and a reliable resource allocation approach at minimum possible cost, for this purpose we used Cascading Failure Resilience System (CSFR) technique. Comparative analyses done to validate our approach and the results shows that our approach handle mitigation failure in an efficient way as well as it provide reliability while providing resources to the users at a comparative low cost.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130148436","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099988
Mohammad A. Abdul Majeed, Omar Munthir Al Okashi, Azmi Tawfeq Alrawi
The regulating organ of the body is the brain. Early diagnosis of brain disorders can have a significant impact on efforts to treat them. A brain hemorrhage is a form of stroke caused by a bursting artery in the brain, resulting in bleeding in the surrounding tissues. Through a brain Computed Tomography (CT) scan, brain hemorrhage can be identified. CT is the most extensively used diagnostic imaging technology for identifying brain illnesses due to its speed, low cost, and wide variety of uses. During a CT scan, a small X-ray beam revolves around the body to capture a sequence of images from different angles. The computer then produces a cross-sectional representation of the body. Intracranial hemorrhage (ICH) is a medical condition that requires prompt identification and treatment. Since ICH early detection and therapy can improve health outcomes, there is a need for a triage system that can immediately identify and speed up the treatment process. In this paper, we will use standard machine learning (Support Vector Machine, Random Forest and Decision Tree) methodologies to present a method for automatically detecting the ICH in a two-dimensional reduced form of a CT scan of the brain. Four main steps make up the method. First, a preprocessing pipeline that can successfully remove the bone from the skull is put into place. The following step is applying a feature extraction method. Then, a suitable feature-selection (PCA) model is proposed, which will enhance the model's performance by minimizing any redundancy produced by the selected feature extraction. The data set from the CT scans is classified into normal and abnormal in the last stage, which involves training and testing a machine learning model. The accuracy for our proposed model using Random Forest (RF), is 92.5%. RF achieves higher performance than other used ML methods.
{"title":"Intracranial hemorrhage detection and classification from CT images based on multiple features and machine learning approaches","authors":"Mohammad A. Abdul Majeed, Omar Munthir Al Okashi, Azmi Tawfeq Alrawi","doi":"10.1109/DeSE58274.2023.10099988","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099988","url":null,"abstract":"The regulating organ of the body is the brain. Early diagnosis of brain disorders can have a significant impact on efforts to treat them. A brain hemorrhage is a form of stroke caused by a bursting artery in the brain, resulting in bleeding in the surrounding tissues. Through a brain Computed Tomography (CT) scan, brain hemorrhage can be identified. CT is the most extensively used diagnostic imaging technology for identifying brain illnesses due to its speed, low cost, and wide variety of uses. During a CT scan, a small X-ray beam revolves around the body to capture a sequence of images from different angles. The computer then produces a cross-sectional representation of the body. Intracranial hemorrhage (ICH) is a medical condition that requires prompt identification and treatment. Since ICH early detection and therapy can improve health outcomes, there is a need for a triage system that can immediately identify and speed up the treatment process. In this paper, we will use standard machine learning (Support Vector Machine, Random Forest and Decision Tree) methodologies to present a method for automatically detecting the ICH in a two-dimensional reduced form of a CT scan of the brain. Four main steps make up the method. First, a preprocessing pipeline that can successfully remove the bone from the skull is put into place. The following step is applying a feature extraction method. Then, a suitable feature-selection (PCA) model is proposed, which will enhance the model's performance by minimizing any redundancy produced by the selected feature extraction. The data set from the CT scans is classified into normal and abnormal in the last stage, which involves training and testing a machine learning model. The accuracy for our proposed model using Random Forest (RF), is 92.5%. RF achieves higher performance than other used ML methods.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115639621","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099597
Y. A. Mashhadany, A. Alrawi, Zeyid T. Ibraheem, Sameer Algburi
Every designer aspires to produce designs that are superior to those of their rivals in terms of quality, speed, or efficiency. Using an ANFIS (Adaptive Neural Inference System) controller and a proportional, integrated, derived (2DO-PID) 2-degree of freedom controller, this study suggests a high-performance design for a 6-DOF manipulator. Finding the best value for the controller settings that smoothly regulate the robot's movements to the desired aim is the primary objective of this exercise. The first step in the design process is to naturally determine the best values for the parameters of a traditional PID controller. The creation of a high-resolution 2DOF-PID controller is the next phase. It performs better than the conventional correct order using a mysterious physics control technique. The parameters of the 2DOF-PID controller are estimated based on the undeniably significant nature of the control effect. The final stage in achieving the high performance of the control system under consideration is the hybrid 2DOF-PID and ANFIS controller, which uses the prior output as a predictive point. The use of both modern and vintage consoles. Six-degree-of-freedom elbow curves are supported. Because the manipulator's trajectory exceeded the settling time and affected the movement, it was possible to minimize. MATLAB 2021b and Robotics Toolbox 9 were used to design and simulate the entire remote-control system. The controller's optimal design is built using a 3-dimensional model of a 6-DOF manipulator created with MATLAB/virtual Simulink's reality (VR) technology. MATLAB generates the manipulator instructions, which are then used to generate a real trajectory with a virtual reality model.
{"title":"Implement of Intelligent Controller for 6DOF Robot Based on a Virtual Reality Model","authors":"Y. A. Mashhadany, A. Alrawi, Zeyid T. Ibraheem, Sameer Algburi","doi":"10.1109/DeSE58274.2023.10099597","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099597","url":null,"abstract":"Every designer aspires to produce designs that are superior to those of their rivals in terms of quality, speed, or efficiency. Using an ANFIS (Adaptive Neural Inference System) controller and a proportional, integrated, derived (2DO-PID) 2-degree of freedom controller, this study suggests a high-performance design for a 6-DOF manipulator. Finding the best value for the controller settings that smoothly regulate the robot's movements to the desired aim is the primary objective of this exercise. The first step in the design process is to naturally determine the best values for the parameters of a traditional PID controller. The creation of a high-resolution 2DOF-PID controller is the next phase. It performs better than the conventional correct order using a mysterious physics control technique. The parameters of the 2DOF-PID controller are estimated based on the undeniably significant nature of the control effect. The final stage in achieving the high performance of the control system under consideration is the hybrid 2DOF-PID and ANFIS controller, which uses the prior output as a predictive point. The use of both modern and vintage consoles. Six-degree-of-freedom elbow curves are supported. Because the manipulator's trajectory exceeded the settling time and affected the movement, it was possible to minimize. MATLAB 2021b and Robotics Toolbox 9 were used to design and simulate the entire remote-control system. The controller's optimal design is built using a 3-dimensional model of a 6-DOF manipulator created with MATLAB/virtual Simulink's reality (VR) technology. MATLAB generates the manipulator instructions, which are then used to generate a real trajectory with a virtual reality model.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124623268","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099802
Faiz Maruf Al Kautsaf, Mohammad Namazee Bin Mohd Nizam, Khalida Shajaratuddur Harun
This paper is about a project to implement a model of point-of-sale (POS) system equipped with Business Intelligence (BI) capabilities that suits the nature of business organisations in the scope of Small and Medium Enterprise (SME) in Indonesia). The project was developed based on proposed framework integrating Point-Of-Sales System, Databases, Visualization Tools namely Microsoft Power BI and its other relevant libraries. Overtime, SMEs in Indonesia have generated large volumes of data from their business operations. The SMEs need to be able to efficiently manage and analyze large volumes of data to provide better business decision making. Effective decision making shall help SMEs achieve competitive advantage. This is where Business Intelligence (BI) comes to light to provide insightful information to facilitate the business decision making process.
本文是关于一个项目，以实现销售点(POS)系统的模型配备商业智能(BI)功能，适合业务组织的性质在印度尼西亚的中小型企业(SME)的范围内)。该项目是基于集成销售点系统、数据库、可视化工具(即Microsoft Power BI)及其其他相关库的拟议框架开发的。随着时间的推移，印尼的中小企业从其业务运营中产生了大量数据。中小企业需要能够有效地管理和分析大量数据，以提供更好的业务决策。有效的决策有助于中小企业获得竞争优势。这就是商业智能(BI)发挥作用的地方，它提供有洞察力的信息，以促进业务决策过程。
{"title":"A System Implementation: Point-of-Sales (POS) System Integrated with Business Intelligence (BI) Capability Focused on SME in Indonesia","authors":"Faiz Maruf Al Kautsaf, Mohammad Namazee Bin Mohd Nizam, Khalida Shajaratuddur Harun","doi":"10.1109/DeSE58274.2023.10099802","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099802","url":null,"abstract":"This paper is about a project to implement a model of point-of-sale (POS) system equipped with Business Intelligence (BI) capabilities that suits the nature of business organisations in the scope of Small and Medium Enterprise (SME) in Indonesia). The project was developed based on proposed framework integrating Point-Of-Sales System, Databases, Visualization Tools namely Microsoft Power BI and its other relevant libraries. Overtime, SMEs in Indonesia have generated large volumes of data from their business operations. The SMEs need to be able to efficiently manage and analyze large volumes of data to provide better business decision making. Effective decision making shall help SMEs achieve competitive advantage. This is where Business Intelligence (BI) comes to light to provide insightful information to facilitate the business decision making process.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"66 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122835543","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100015
Sarah Rowlands, D. Al-Jumeily, S. Assi
Counterfeit medicinal and lifestyles products are a global issue that impacts public health. Counterfeit products are often made in unsafe and unsanitary conditions before their release to the public without testing by regulatory bodies. One product that is particularly susceptible to online counterfeiting is Viagra, which is one of the highest selling medicines worldwide. A total of 57 Viagra tablets were used for the study; this included 27 authentic and 30 counterfeit tablets which were measured using near-infrared spectroscopy (NIRS). Spectra obtained using the NIR spectrometer non-destructively were exported into a multi-paradigm numerical computing environment where machine learning algorithms (MLAs) were applied using Matlab 2007a. Four algorithms were used related to correlation in wavelength space (CWS), K-nearest neighbour (KNN), principal component analysis (PCA) and PCA combined with fuzzy C-mean clustering (PCA-FCM). The algorithms were applied unsupervised to the authentic and counterfeit tables with no prior labelling to any of the tablets. The results showed two clear groups/clusters between the authentic and counterfeit tablets. In particular, PCA and PCA-FCM showed further subgroups among the counterfeit tablets that corresponded to their varying manufacturing sources. In summary, the use of NIRS and MLAs proved an effective method for identifying counterfeit Viagra medicines rapidly and non-destructively.
{"title":"Identification of authentic and counterfeit Viagra tablets using near-infrared spectroscopic methods and machine learning algorithms","authors":"Sarah Rowlands, D. Al-Jumeily, S. Assi","doi":"10.1109/DeSE58274.2023.10100015","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100015","url":null,"abstract":"Counterfeit medicinal and lifestyles products are a global issue that impacts public health. Counterfeit products are often made in unsafe and unsanitary conditions before their release to the public without testing by regulatory bodies. One product that is particularly susceptible to online counterfeiting is Viagra, which is one of the highest selling medicines worldwide. A total of 57 Viagra tablets were used for the study; this included 27 authentic and 30 counterfeit tablets which were measured using near-infrared spectroscopy (NIRS). Spectra obtained using the NIR spectrometer non-destructively were exported into a multi-paradigm numerical computing environment where machine learning algorithms (MLAs) were applied using Matlab 2007a. Four algorithms were used related to correlation in wavelength space (CWS), K-nearest neighbour (KNN), principal component analysis (PCA) and PCA combined with fuzzy C-mean clustering (PCA-FCM). The algorithms were applied unsupervised to the authentic and counterfeit tables with no prior labelling to any of the tablets. The results showed two clear groups/clusters between the authentic and counterfeit tablets. In particular, PCA and PCA-FCM showed further subgroups among the counterfeit tablets that corresponded to their varying manufacturing sources. In summary, the use of NIRS and MLAs proved an effective method for identifying counterfeit Viagra medicines rapidly and non-destructively.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394632","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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