Pub Date : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029398
P. Vichare, M. Cano, Keshav P. Dahal, Tomasz Siewierski, Marco Gilardi
The basic requirement in curriculum design is to review and constructively align programme modules with state-of-the art and trends in the subject area. Extended reality (XR), an umbrella term for emerging technologies such as augmented reality (AR), mixed reality (MR) and virtual reality (VR), is becoming a prominent aspect of design and visualization for architecture, engineering, and construction (AEC) industry. Engineering programmes are primary feeders for the AEC industry, delivery of CAD and visualisation modules provide wider opportunities for adapting such emerging technologies in the subject area, as well as use of these technologies for developing novel pedagogical practices. This paper provides a rational behind revising traditional CAD and visualisation modules designed for Engineering undergraduate programmes, and constructively incorporate XR within programme modules. A critical literature review is provided on XR subject area as well as XR based pedagogical practices. This review identifies elements of XR as a subject area that can be incorporated in AEC programmes. It also highlights academic and operational considerations in adapting XR technology for delivering CAD and visualisation modules. Similar approach is extended to evaluate integration of XR technologies for Electrical and Power Engineering Programmes.
{"title":"Incorporating Extended Reality Technology for Delivering Computer Aided Design and Visualisation Modules","authors":"P. Vichare, M. Cano, Keshav P. Dahal, Tomasz Siewierski, Marco Gilardi","doi":"10.1109/SKIMA57145.2022.10029398","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029398","url":null,"abstract":"The basic requirement in curriculum design is to review and constructively align programme modules with state-of-the art and trends in the subject area. Extended reality (XR), an umbrella term for emerging technologies such as augmented reality (AR), mixed reality (MR) and virtual reality (VR), is becoming a prominent aspect of design and visualization for architecture, engineering, and construction (AEC) industry. Engineering programmes are primary feeders for the AEC industry, delivery of CAD and visualisation modules provide wider opportunities for adapting such emerging technologies in the subject area, as well as use of these technologies for developing novel pedagogical practices. This paper provides a rational behind revising traditional CAD and visualisation modules designed for Engineering undergraduate programmes, and constructively incorporate XR within programme modules. A critical literature review is provided on XR subject area as well as XR based pedagogical practices. This review identifies elements of XR as a subject area that can be incorporated in AEC programmes. It also highlights academic and operational considerations in adapting XR technology for delivering CAD and visualisation modules. Similar approach is extended to evaluate integration of XR technologies for Electrical and Power Engineering Programmes.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114043297","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029568
K. A. Khaliq, C. Noakes, Andrew H. Kemp, Carl Thompson
Many of us spend large amounts of time indoors, so indoor air quality (IAQ) improves our quality of life. IAQ is affected by contextual, occupant, and building-related (COB) factors. We know little of the effects of IAQ on comfort and wellbeing in the elderly and there is almost no data on air quality measured in residential nursing homes. Technological advances in ambient assisted living and the Internet of Things (IoT), make it possible to build objects with the capacity to monitor IAQ in real time. In this study, we used IoT-based sensors in two nursing homes to assess IAQ by monitoring CO2, temperature, and humidity during the Summer of 2022, taking into account the outdoor weather conditions and the need for thermal appliances or the airflow from windows. The presence of residents and workers in communal areas raised CO2 levels with windows closed, whilst opening them improves the air quality. Our results show how opening windows in communal spaces in elderly care environments can help preserve indoor air quality (IAQ) when occupancy is high. These “simple” solutions to raising IAQ rely on overcoming behavioural, technical and data-related challenges - which we discuss.
{"title":"Indoor Air Quality Assessment using IoT-based Sensors in Nursing Homes","authors":"K. A. Khaliq, C. Noakes, Andrew H. Kemp, Carl Thompson","doi":"10.1109/SKIMA57145.2022.10029568","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029568","url":null,"abstract":"Many of us spend large amounts of time indoors, so indoor air quality (IAQ) improves our quality of life. IAQ is affected by contextual, occupant, and building-related (COB) factors. We know little of the effects of IAQ on comfort and wellbeing in the elderly and there is almost no data on air quality measured in residential nursing homes. Technological advances in ambient assisted living and the Internet of Things (IoT), make it possible to build objects with the capacity to monitor IAQ in real time. In this study, we used IoT-based sensors in two nursing homes to assess IAQ by monitoring CO2, temperature, and humidity during the Summer of 2022, taking into account the outdoor weather conditions and the need for thermal appliances or the airflow from windows. The presence of residents and workers in communal areas raised CO2 levels with windows closed, whilst opening them improves the air quality. Our results show how opening windows in communal spaces in elderly care environments can help preserve indoor air quality (IAQ) when occupancy is high. These “simple” solutions to raising IAQ rely on overcoming behavioural, technical and data-related challenges - which we discuss.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116815038","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029567
Shiva Prasad Dahal, M. Dahal, B. Silwal
This paper discusses the health monitoring of synchronous generators used in hydropower plants. In recent years, maintenance of generating stations has shifted its focus from preventive maintenance to predictive maintenance. Machine prognosis is a significant part of condition-based maintenance. It intends to monitor and track the time evolution of a fault, so that maintenance can be performed, or the task can be terminated to avoid a catastrophic failure. This paper focuses on the machine learning model for health detection of stator winding of synchronous generator by using stator terminal voltage and stator winding current as input and stator winding temperature as output. More than five years of real-time data of a synchronous generator of Sardikhola hydropower plant in Nepal are collected to predict and present the Adaptive Neuro-Fuzzy Interference System (ANFIS) model. This model predicts faulty data range and healthy data range of stator winding temperature corresponding to stator terminal voltage and current.
{"title":"Historical Data Based Monitoring of Hydro Generator Using Machine Learning","authors":"Shiva Prasad Dahal, M. Dahal, B. Silwal","doi":"10.1109/SKIMA57145.2022.10029567","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029567","url":null,"abstract":"This paper discusses the health monitoring of synchronous generators used in hydropower plants. In recent years, maintenance of generating stations has shifted its focus from preventive maintenance to predictive maintenance. Machine prognosis is a significant part of condition-based maintenance. It intends to monitor and track the time evolution of a fault, so that maintenance can be performed, or the task can be terminated to avoid a catastrophic failure. This paper focuses on the machine learning model for health detection of stator winding of synchronous generator by using stator terminal voltage and stator winding current as input and stator winding temperature as output. More than five years of real-time data of a synchronous generator of Sardikhola hydropower plant in Nepal are collected to predict and present the Adaptive Neuro-Fuzzy Interference System (ANFIS) model. This model predicts faulty data range and healthy data range of stator winding temperature corresponding to stator terminal voltage and current.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124935028","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029462
P. Tiwari, Amar V. Desai, M. Chavan, P. Sureephong, Sylvain Touchard
This paper discusses different drying methods for herbs and vegetables and demonstrates the benefits of using a dehydrator over other methods. Not all temperatures are ideal for drying herbs; there are various precautions and specific temperatures to obtain high-quality herbs. It also discusses the smart dehydrator, which is built with a PID control system and can be controlled by an IOT platform. Arduino Uno is responsible for the PID control system. This dehydrator is a smart dehydrator. Various herbs were dried as a sample and yielded positive results.
{"title":"Drying Herbs with a Smart Dehydrator","authors":"P. Tiwari, Amar V. Desai, M. Chavan, P. Sureephong, Sylvain Touchard","doi":"10.1109/SKIMA57145.2022.10029462","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029462","url":null,"abstract":"This paper discusses different drying methods for herbs and vegetables and demonstrates the benefits of using a dehydrator over other methods. Not all temperatures are ideal for drying herbs; there are various precautions and specific temperatures to obtain high-quality herbs. It also discusses the smart dehydrator, which is built with a PID control system and can be controlled by an IOT platform. Arduino Uno is responsible for the PID control system. This dehydrator is a smart dehydrator. Various herbs were dried as a sample and yielded positive results.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125118750","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029587
A. Darlington-NjokuChidinma, B. Mishra, William K. P. Sayers
In recent years, various industries have been on the quest to derive new knowledge and information from the data they produce. When these data are well utilised, they can create frameworks for improving business processes, product quality, and services. However, more often, data are in unstructured and semi-structured data formats. Because of this, the discovery of critical issues within textual data becomes challenging. In the past few years, the adoption of natural language prepossessing (NLP) and machine learning (ML) techniques are increasingly becoming popular for exploring knowledge within text documents that could help decision-makers and experts to solve business challenges and improve their business processes and systems. This research is being experimented with NLP and ML on the fault log of a UK-based commercial MRO (Maintenance, Repair, and Overhaul) provider in the Aerospace Industry to support decision-making. The first stage systematically leverages text analysis to extract valuable information from many customers' fault notifications, compares its similarity with the expert's maintenance action, and then classifies them into three categories which are Modification, Replacement, and No-fault-found. In the second phase, the extracted features get fed into the machine learner to categorise and predict future faults diagnosis in commercial aircraft’ FQIS (Fuel Quantity Indicating System) to automate troubleshooting, support maintenance operations, and improve decision-making in MRO services.
{"title":"Fault Log Text Classification Using Natural Language Processing And Machine Learning For Decision Support","authors":"A. Darlington-NjokuChidinma, B. Mishra, William K. P. Sayers","doi":"10.1109/SKIMA57145.2022.10029587","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029587","url":null,"abstract":"In recent years, various industries have been on the quest to derive new knowledge and information from the data they produce. When these data are well utilised, they can create frameworks for improving business processes, product quality, and services. However, more often, data are in unstructured and semi-structured data formats. Because of this, the discovery of critical issues within textual data becomes challenging. In the past few years, the adoption of natural language prepossessing (NLP) and machine learning (ML) techniques are increasingly becoming popular for exploring knowledge within text documents that could help decision-makers and experts to solve business challenges and improve their business processes and systems. This research is being experimented with NLP and ML on the fault log of a UK-based commercial MRO (Maintenance, Repair, and Overhaul) provider in the Aerospace Industry to support decision-making. The first stage systematically leverages text analysis to extract valuable information from many customers' fault notifications, compares its similarity with the expert's maintenance action, and then classifies them into three categories which are Modification, Replacement, and No-fault-found. In the second phase, the extracted features get fed into the machine learner to categorise and predict future faults diagnosis in commercial aircraft’ FQIS (Fuel Quantity Indicating System) to automate troubleshooting, support maintenance operations, and improve decision-making in MRO services.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305566","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029556
Marzia Hoque Tania, M. Kaiser, A. Shabut, Kamal Abu-Hassan, M. Mahmud, M. A. Hossain
This paper performs an investigation into the development of an intelligent image-based automatic in vitro diagnostic system for infectious diseases using personal devices. The proposed framework of the image-based diagnostic system is demonstrated using the case study of Tuberculosis (TB)-specific antibody detection. The developed system, denoted as the iTB-test, is an intelligent bio-sensing system, comprised of a plasmonic Enzyme-Linked Immunosorbent Assay based colourimetric test in combination with an artificial intelligence-enabled image-based system. The presented system can separate the region of interest with 99.62% accuracy using clustering-based hybrid image processing algorithms, whereas the classification accuracy of antibody detection using a supervised machine learning technique is 100% based on the experiments conducted for the case study.
{"title":"iTB-test: An Intelligent Image-enabled Diagnostic System for In Vitro Screening of Infectious Diseases","authors":"Marzia Hoque Tania, M. Kaiser, A. Shabut, Kamal Abu-Hassan, M. Mahmud, M. A. Hossain","doi":"10.1109/SKIMA57145.2022.10029556","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029556","url":null,"abstract":"This paper performs an investigation into the development of an intelligent image-based automatic in vitro diagnostic system for infectious diseases using personal devices. The proposed framework of the image-based diagnostic system is demonstrated using the case study of Tuberculosis (TB)-specific antibody detection. The developed system, denoted as the iTB-test, is an intelligent bio-sensing system, comprised of a plasmonic Enzyme-Linked Immunosorbent Assay based colourimetric test in combination with an artificial intelligence-enabled image-based system. The presented system can separate the region of interest with 99.62% accuracy using clustering-based hybrid image processing algorithms, whereas the classification accuracy of antibody detection using a supervised machine learning technique is 100% based on the experiments conducted for the case study.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126317538","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029469
Noha M. Hamza, S. Elsayed, R. Sarker, D. Essam
Many real-life optimization problems involve dynamic changes with uncertain parameters and data, which make the decision-making process challenging. Although there are some studies on solving dynamic or uncertain problems, there is limited work on solving problems with both dynamic and uncertain characteristics. Therefore, this paper proposes an evolutionary framework for solving constrained optimization problems where the objective function's coefficients are uncertain and changing over time. In the algorithm, a mechanism is proposed for detecting a change and predicting the magnitude of uncertainty, which helps to generate better initial solutions for the evolutionary search process that improves its performance after a dynamic change. It is evaluated on 13 benchmark problems, with the reported results demonstrating its efficiency in terms of the quality of its solutions.
{"title":"Evolutionary Constrained Optimization with Dynamic Changes and Uncertainty in the Objective Function","authors":"Noha M. Hamza, S. Elsayed, R. Sarker, D. Essam","doi":"10.1109/SKIMA57145.2022.10029469","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029469","url":null,"abstract":"Many real-life optimization problems involve dynamic changes with uncertain parameters and data, which make the decision-making process challenging. Although there are some studies on solving dynamic or uncertain problems, there is limited work on solving problems with both dynamic and uncertain characteristics. Therefore, this paper proposes an evolutionary framework for solving constrained optimization problems where the objective function's coefficients are uncertain and changing over time. In the algorithm, a mechanism is proposed for detecting a change and predicting the magnitude of uncertainty, which helps to generate better initial solutions for the evolutionary search process that improves its performance after a dynamic change. It is evaluated on 13 benchmark problems, with the reported results demonstrating its efficiency in terms of the quality of its solutions.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131896421","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029420
Daravichet Tin, M. Shahpasand, H. Gharakheili, Gustavo E. A. P. A. Batista
The continuous rise of traffic encryption in IoT devices has led network operators to revisit the way they gain visibility into the behavior of their network and connected assets. Moreover, flow-level analysis is perceived as a more cost-effective approach in network monitoring, particularly at scale, given the high computing cost of deep packet inspection engines. This paper uses time-series signals captured from the flow activity of IoT devices and classifies network traffic with deep learning-based classifiers based on Neural Networks (NN) and Decision Trees (DT). We analyze the efficiency and efficacy of deep learning models using one-dimensional convolutional neural networks (1D-CNN), Long Short Term Memory (LSTM), and Deep Forest (DF). We train our models on the real network traffic of 10 IoT devices collected from our lab during two months. To the best of our knowledge, this study is the first to investigate the performance of DF classifiers on IoT network traffic data and compare them to deep neural network models. We quantify the performance of our models by varying the window size (one minute to three minutes) in a time-series format. We show that the DF models present similar performance to 1D-CNN and LSTM and outperform the (shallow) Random Forest (RF) model but significantly higher inference time. DFs are attractive models since they have a dynamic architecture adjusted during training. Therefore, there is no need to manually search for the model architecture required for deep neural networks.
{"title":"Classifying Time-Series of IoT Flow Activity using Deep Learning and Intransitive Features","authors":"Daravichet Tin, M. Shahpasand, H. Gharakheili, Gustavo E. A. P. A. Batista","doi":"10.1109/SKIMA57145.2022.10029420","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029420","url":null,"abstract":"The continuous rise of traffic encryption in IoT devices has led network operators to revisit the way they gain visibility into the behavior of their network and connected assets. Moreover, flow-level analysis is perceived as a more cost-effective approach in network monitoring, particularly at scale, given the high computing cost of deep packet inspection engines. This paper uses time-series signals captured from the flow activity of IoT devices and classifies network traffic with deep learning-based classifiers based on Neural Networks (NN) and Decision Trees (DT). We analyze the efficiency and efficacy of deep learning models using one-dimensional convolutional neural networks (1D-CNN), Long Short Term Memory (LSTM), and Deep Forest (DF). We train our models on the real network traffic of 10 IoT devices collected from our lab during two months. To the best of our knowledge, this study is the first to investigate the performance of DF classifiers on IoT network traffic data and compare them to deep neural network models. We quantify the performance of our models by varying the window size (one minute to three minutes) in a time-series format. We show that the DF models present similar performance to 1D-CNN and LSTM and outperform the (shallow) Random Forest (RF) model but significantly higher inference time. DFs are attractive models since they have a dynamic architecture adjusted during training. Therefore, there is no need to manually search for the model architecture required for deep neural networks.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122292935","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 : 2022-12-02DOI: 10.1109/SKIMA57145.2022.10029406
Ali Elmhamudi, Aliyu Abubakar, H. Ugail, Brian Thomson, C. Wilson, Mark Turner, D. Manas, S. Tingle, S. Colenutt, G. Sen, Jim Hunter, Meng Sun, Jackie Scully
The kidney is a vital organ in humans that removes toxic waste from the body and maintains the balance between water, minerals, and salts. Malfunctioning of this vital organ has become one of the significant public health concerns in recent years. The most viable way to treat patients with acute kidney failure is via transplantation. A healthy substitute is required from a healthy donor, which goes through rigorous examination by experienced clinicians to ascertain its vitality. However, the whole procedure is time-consuming, not reliable, and has high intra-observer and inter-observer variations. For these reasons, we proposed a machine learning-based approach using photographic samples to assess the health of the donor organ. Deep learning models, VGG-16 and DenseNet121, were used for feature extraction from 120 organs labelled 1,2,3,4 and 5, where scores 1 and 2 are good, score 3 is fair (uncertain), and 4 and 5 as poor. Random Forest Regressor and Support Vector Regressor were trained and then used to predict the surgeon-derived score labels, determining whether an organ is transplantable or should be discarded. The results indicate an algorithm of this nature could go a long way show in deciding the transplantability of a kidney organ.
{"title":"Deep Learning Assisted Kidney Organ Image Analysis for Assessing the Viability of Transplantation","authors":"Ali Elmhamudi, Aliyu Abubakar, H. Ugail, Brian Thomson, C. Wilson, Mark Turner, D. Manas, S. Tingle, S. Colenutt, G. Sen, Jim Hunter, Meng Sun, Jackie Scully","doi":"10.1109/SKIMA57145.2022.10029406","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029406","url":null,"abstract":"The kidney is a vital organ in humans that removes toxic waste from the body and maintains the balance between water, minerals, and salts. Malfunctioning of this vital organ has become one of the significant public health concerns in recent years. The most viable way to treat patients with acute kidney failure is via transplantation. A healthy substitute is required from a healthy donor, which goes through rigorous examination by experienced clinicians to ascertain its vitality. However, the whole procedure is time-consuming, not reliable, and has high intra-observer and inter-observer variations. For these reasons, we proposed a machine learning-based approach using photographic samples to assess the health of the donor organ. Deep learning models, VGG-16 and DenseNet121, were used for feature extraction from 120 organs labelled 1,2,3,4 and 5, where scores 1 and 2 are good, score 3 is fair (uncertain), and 4 and 5 as poor. Random Forest Regressor and Support Vector Regressor were trained and then used to predict the surgeon-derived score labels, determining whether an organ is transplantable or should be discarded. The results indicate an algorithm of this nature could go a long way show in deciding the transplantability of a kidney organ.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134174139","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}
Classification of Imbalance data is one of t he most vital tasks in the field of machine learning because most of the real-life datasets available have an imbalanced distribution of class labels. The effect of imbalanced data is severe where the predictive model trained on the imbalanced data faces some unprecedented problems like overfitting where t he model gets biased towards the majority target class. Many techniques have been proposed over time to deal with the imbalanced distribution caused by problems like oversampling and undersampling where oversampling isn't able to match the performance acquired by the undersampling method. One such baseline method is clustering the majority of data into multiple clusters and then randomly sampling some of the redundant data but we believe that randomly sampling the data sample might open the loophole to losing informative data samples. So, in this work, we would like to propose two clustering-based priority sampling methods which manage to boost the performance of the predictive model compared to the clustering-based random sampling techniques.
{"title":"A Clustering Based Priority Driven Sampling Technique for Imbalance Data Classification","authors":"Iftakhar Ali Khandokar, Abdullah-All-Tanvir, Tanvina Khondokar, Nabila Tabassum Jhilik, Swakkhar Shatabda","doi":"10.1109/SKIMA57145.2022.10029565","DOIUrl":"https://doi.org/10.1109/SKIMA57145.2022.10029565","url":null,"abstract":"Classification of Imbalance data is one of t he most vital tasks in the field of machine learning because most of the real-life datasets available have an imbalanced distribution of class labels. The effect of imbalanced data is severe where the predictive model trained on the imbalanced data faces some unprecedented problems like overfitting where t he model gets biased towards the majority target class. Many techniques have been proposed over time to deal with the imbalanced distribution caused by problems like oversampling and undersampling where oversampling isn't able to match the performance acquired by the undersampling method. One such baseline method is clustering the majority of data into multiple clusters and then randomly sampling some of the redundant data but we believe that randomly sampling the data sample might open the loophole to losing informative data samples. So, in this work, we would like to propose two clustering-based priority sampling methods which manage to boost the performance of the predictive model compared to the clustering-based random sampling techniques.","PeriodicalId":277436,"journal":{"name":"2022 14th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113967335","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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