PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021最新文献
This study aims to review studies related to IoT maturity models in manufacturing systematically. Digitalization is vital for small and medium enterprises (SMEs) to retain a competitive advantage, reduce their operational expenses, and compete with larger firms in their respective market. Globally IoT-induced transformation in manufacturing has been significant. Due to the lack of resources, American Automotive SMEs have not been farsighted with regards to digitalization but benefits like improved workflows, efficiencies, reduced overheads, and value creation make it beneficial and invaluable to customers. This paper reviews the state of IoT applications in American Automotive SMEs using the assessment provided by the IoT maturity model.
{"title":"Review of Digitalization using IoT Maturity Models: The Case of American Automotive SMEs","authors":"Dharmender Salian","doi":"10.58190/icat.2023.34","DOIUrl":"https://doi.org/10.58190/icat.2023.34","url":null,"abstract":"This study aims to review studies related to IoT maturity models in manufacturing systematically. Digitalization is vital for small and medium enterprises (SMEs) to retain a competitive advantage, reduce their operational expenses, and compete with larger firms in their respective market. Globally IoT-induced transformation in manufacturing has been significant. Due to the lack of resources, American Automotive SMEs have not been farsighted with regards to digitalization but benefits like improved workflows, efficiencies, reduced overheads, and value creation make it beneficial and invaluable to customers. This paper reviews the state of IoT applications in American Automotive SMEs using the assessment provided by the IoT maturity model.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969522","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}
Zar Chi Linn, War War Min Swe, Aung Kyaw Soe, Aung Ko Latt
This paper investigated the thermal performance of a solar air collector with attached rectangular fins both theoretically and experimentally. The effects of mass flow rate (0.015 kg/s to 0.06 kg/s) and fin spacing on thermal performance and temperature rise were investigated using varying solar radiation intensity (500, 600, 700, and 800 W/m2). The results show that, when using a finned absorber instead of a plane absorber, the maximum thermal efficiency of 1.264 times was achieved. Additionally, at a lower mass flow rate of 0.015 kg/s, a maximum increase in temperature rise has been observed to be 1.243 times greater than that of a plane absorber.
{"title":"Analysis of Thermal Performance of Double Flow Finned Absorber Solar Air Heater","authors":"Zar Chi Linn, War War Min Swe, Aung Kyaw Soe, Aung Ko Latt","doi":"10.58190/icat.2023.29","DOIUrl":"https://doi.org/10.58190/icat.2023.29","url":null,"abstract":"This paper investigated the thermal performance of a solar air collector with attached rectangular fins both theoretically and experimentally. The effects of mass flow rate (0.015 kg/s to 0.06 kg/s) and fin spacing on thermal performance and temperature rise were investigated using varying solar radiation intensity (500, 600, 700, and 800 W/m2). The results show that, when using a finned absorber instead of a plane absorber, the maximum thermal efficiency of 1.264 times was achieved. Additionally, at a lower mass flow rate of 0.015 kg/s, a maximum increase in temperature rise has been observed to be 1.243 times greater than that of a plane absorber.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969524","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}
Today, as the demand for electrical energy increases, the demand for renewable energy, which is a clean energy source, continues to increase. Solar inverters used in solar power plants are devices that convert DC power to AC. Solar inverters are divided into three groups as central inverter, micro inverter and string inverter. String inverter model is used in this study. It has been determined that as a result of incorrectly connecting the string cables during installation and workmanship in the string inverter, the solar inverter does not work at full capacity and full efficiency cannot be obtained at its output. A problem experienced during the connection and installation phase causes inefficiency in the system and inability to get full efficiency from the inverter. For this reason, in this study, MPPT string cable arrangement, which is connected incorrectly in a string inverter, has been carried out and the production has been increased to 96.72 kW.
{"title":"MPPT String Arrangement and Production Improvements in String Inverters","authors":"Hale Bakır","doi":"10.58190/icat.2023.19","DOIUrl":"https://doi.org/10.58190/icat.2023.19","url":null,"abstract":"Today, as the demand for electrical energy increases, the demand for renewable energy, which is a clean energy source, continues to increase. Solar inverters used in solar power plants are devices that convert DC power to AC. Solar inverters are divided into three groups as central inverter, micro inverter and string inverter. String inverter model is used in this study. It has been determined that as a result of incorrectly connecting the string cables during installation and workmanship in the string inverter, the solar inverter does not work at full capacity and full efficiency cannot be obtained at its output. A problem experienced during the connection and installation phase causes inefficiency in the system and inability to get full efficiency from the inverter. For this reason, in this study, MPPT string cable arrangement, which is connected incorrectly in a string inverter, has been carried out and the production has been increased to 96.72 kW.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969513","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}
Tuberculosis is an infectious disease caused by bacteria called Mycobacterium tuberculosis. Tuberculosis is still an important public health problem worldwide and is common especially in developing countries. This respiratory disease can cause serious symptoms, especially affecting the lungs. Symptoms of tuberculosis include prolonged coughing, shortness of breath, chest pain, weakness, fever, night sweats, and malaise. The diagnosis of the disease is made by clinical signs as well as biomedical imaging methods and laboratory tests. These imaging modalities include techniques such as x-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Early diagnosis of tuberculosis disease is of great importance in terms of treatment and prevention of the spread of the disease. The use of deep learning methods to classify biomedical images of tuberculosis disease can accelerate the diagnosis process, increase accuracy and guide treatment more effectively. In this study, it aims to be an important step in the classification of tuberculosis disease with deep learning. The generated CNN network, parameter values, layers used, complexity matrices obtained for verification data, accuracy and loss graphs are shown in detail. In our study, the success rate was increased by using a different network structure than the neural networks used in the literature. Approximately 98% success was achieved with the proposed CNN model.
{"title":"Detection of Tuberculosis Disease with Convolutional Neural Networks","authors":"Mehmet BABALIK, Çiğdem BAKIR","doi":"10.58190/icat.2023.17","DOIUrl":"https://doi.org/10.58190/icat.2023.17","url":null,"abstract":"Tuberculosis is an infectious disease caused by bacteria called Mycobacterium tuberculosis. Tuberculosis is still an important public health problem worldwide and is common especially in developing countries. This respiratory disease can cause serious symptoms, especially affecting the lungs. Symptoms of tuberculosis include prolonged coughing, shortness of breath, chest pain, weakness, fever, night sweats, and malaise. The diagnosis of the disease is made by clinical signs as well as biomedical imaging methods and laboratory tests. These imaging modalities include techniques such as x-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Early diagnosis of tuberculosis disease is of great importance in terms of treatment and prevention of the spread of the disease. The use of deep learning methods to classify biomedical images of tuberculosis disease can accelerate the diagnosis process, increase accuracy and guide treatment more effectively. In this study, it aims to be an important step in the classification of tuberculosis disease with deep learning. The generated CNN network, parameter values, layers used, complexity matrices obtained for verification data, accuracy and loss graphs are shown in detail. In our study, the success rate was increased by using a different network structure than the neural networks used in the literature. Approximately 98% success was achieved with the proposed CNN model.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969516","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}
Online retail companies focus on two activities for getting revenue in their businesses and to survive in the market. First activity is increasing traffic of the online shopping platform and second activity is converting this traffic to revenue for the company. Marketing facilities try to attract customers to the online shopping platforms at great costs. Because of the costs of getting traffic, it is crucial to make customers order. Online shopping platforms need to understand which factors are decisive on customer purchase decision. In this study, which factors are decisive on consumer purchase decisions will be studied on an e-commerce retail platform from Turkey, Hepsiburada. Which of these factors are most decisive try to be defined: traffic source type (google, campaign, direct etc.) of the customer, customer persona or segment, which types of page or page components has been seen, product position on the page, does the customer benefited from campaign or discount, product review scores and counts, has the product recommended or not. In this study, data will be gathered from Hepsiburada transactions stored in google's big query environment. Performance problems will be solved via SQL optimization and other methods. Data quality issues will be fixed to get consistent results. Then statistical methods, supervised machine learning and deep learning methods will be applied to data for getting feature importances. Importance value of the features will show which factor decisive on customer purchase decision. Feature importance values will be compared and evaluated according to method, model results. Hyperparameter tunings is applied to the methods. Also, the model performances will be compared and evaluated. This study uses and compares 7 methods and there is no comprehensive study in literature in terms of method variety.
{"title":"Defining The Decisive Factors on Purchase and Comparing Feature Importance Methods","authors":"Erman Demir, F. Serhan Daniş","doi":"10.58190/icat.2023.18","DOIUrl":"https://doi.org/10.58190/icat.2023.18","url":null,"abstract":"Online retail companies focus on two activities for getting revenue in their businesses and to survive in the market. First activity is increasing traffic of the online shopping platform and second activity is converting this traffic to revenue for the company. Marketing facilities try to attract customers to the online shopping platforms at great costs. Because of the costs of getting traffic, it is crucial to make customers order. Online shopping platforms need to understand which factors are decisive on customer purchase decision. In this study, which factors are decisive on consumer purchase decisions will be studied on an e-commerce retail platform from Turkey, Hepsiburada. Which of these factors are most decisive try to be defined: traffic source type (google, campaign, direct etc.) of the customer, customer persona or segment, which types of page or page components has been seen, product position on the page, does the customer benefited from campaign or discount, product review scores and counts, has the product recommended or not. In this study, data will be gathered from Hepsiburada transactions stored in google's big query environment. Performance problems will be solved via SQL optimization and other methods. Data quality issues will be fixed to get consistent results. Then statistical methods, supervised machine learning and deep learning methods will be applied to data for getting feature importances. Importance value of the features will show which factor decisive on customer purchase decision. Feature importance values will be compared and evaluated according to method, model results. Hyperparameter tunings is applied to the methods. Also, the model performances will be compared and evaluated. This study uses and compares 7 methods and there is no comprehensive study in literature in terms of method variety.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969518","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}
The T-history method, first proposed by Zhang & Jiang, is a simple method used to determine the latent heat, specific heat, thermal conductivity, melting point, and degree of supercooling of a phase-change material (PCM). The method is based on the measurement of the temperature of the material over time. It assumes that the PCM sample and the reference material exchange thermal energy with the environment in a similar manner, because it is based on the lumped capacitance method, and the temperature distribution inside the sample is uniform. There are multiple advantages that the T history method has over conventional methods; however, the T history method also has some downfalls, which have been improved over the years by different authors. This study experimentally investigates the reliability of this technique for PCM with a phase change temperature below 0 ° C. The technique is used to first determine the properties of denoised water with a phase change temperature of 0 ° C, using ethylene glycol with a mixture ratio of 50/50 as reference material. Second, by determining the properties of KCl with a concentration of 19.5% salt to water for a phase change temperature of -10 ° C. Two analytical approaches were used, one by immersing the samples in antifreeze during the charging process and the other by exposing the samples to ambient air during the discharging phase. The experimental results were further validated using the known literature to determine the latent heat of KCl. This study found that the latent heat of KCl measured using the technique has a 6.2% difference from the known results determined by the DSC. This study recommends the use of the T history method when designing latent heat thermal energy storage systems (LHTESS), because the sample size is an important characteristic for accurate thermophysical properties during the design process.
{"title":"Investigation of T-history Method Application for Sub-zero Phase Change Material Thermal Properties","authors":"Thandiwe Bongani Radebe, Asasei Unarine Casey Ndanduleni, Zhongjie Huan","doi":"10.58190/icat.2023.31","DOIUrl":"https://doi.org/10.58190/icat.2023.31","url":null,"abstract":"The T-history method, first proposed by Zhang & Jiang, is a simple method used to determine the latent heat, specific heat, thermal conductivity, melting point, and degree of supercooling of a phase-change material (PCM). The method is based on the measurement of the temperature of the material over time. It assumes that the PCM sample and the reference material exchange thermal energy with the environment in a similar manner, because it is based on the lumped capacitance method, and the temperature distribution inside the sample is uniform. There are multiple advantages that the T history method has over conventional methods; however, the T history method also has some downfalls, which have been improved over the years by different authors. This study experimentally investigates the reliability of this technique for PCM with a phase change temperature below 0 ° C. The technique is used to first determine the properties of denoised water with a phase change temperature of 0 ° C, using ethylene glycol with a mixture ratio of 50/50 as reference material. Second, by determining the properties of KCl with a concentration of 19.5% salt to water for a phase change temperature of -10 ° C. Two analytical approaches were used, one by immersing the samples in antifreeze during the charging process and the other by exposing the samples to ambient air during the discharging phase. The experimental results were further validated using the known literature to determine the latent heat of KCl. This study found that the latent heat of KCl measured using the technique has a 6.2% difference from the known results determined by the DSC. This study recommends the use of the T history method when designing latent heat thermal energy storage systems (LHTESS), because the sample size is an important characteristic for accurate thermophysical properties during the design process.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969527","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}
Along with the Internet, digital technologies are frequently used in every moment of our lives. Many transactions that we carry out in monetary terms such as shopping in our daily life are now done digitally. With the developing digitalization in the world, people's lives become easier and people can access different products in a short time. In particular, people can spend and shop quickly and easily without carrying cash in their pockets with a credit card. However, with the increase in the use of credit cards, there are also some security vulnerabilities. Fraudsters can gain unfair advantage by obtaining certain credit card information such as passwords. They can shop with someone else's credit card without permission. These transactions cause substantial financial damage to individuals and institutions. With the increase in the use of credit cards with the developing technology, such credit card fraud is also increasing rapidly. Taking precautions against credit card fraud is a very important issue in order to ensure the safety of people. For this reason, in order to ensure the security of both banks and financial institutions that provide credit card services, it is necessary to prevent credit card fraud and to detect fraud that may occur in credit cards within the scope of combating fraud. In our study, Artificial Neural Networks were used to detect credit card fraud transactions. A prediction model has been developed to detect fraud in credit card transactions with ANN. Using the Credit Card data set obtained from the Kaggle database, modeling was done with the Feed Forward Artificial Neural Network method. The aim of this study is to automatically detect abnormal behaviors made with credit cards. 98.44% success was achieved with feedforward artificial neural network.
{"title":"Detection of Credit Card Fraud with Artificial Neural Networks","authors":"Ferhat YEŞİLYURT, Hasan TEMURTAŞ, Çiğdem BAKIR","doi":"10.58190/icat.2023.15","DOIUrl":"https://doi.org/10.58190/icat.2023.15","url":null,"abstract":"Along with the Internet, digital technologies are frequently used in every moment of our lives. Many transactions that we carry out in monetary terms such as shopping in our daily life are now done digitally. With the developing digitalization in the world, people's lives become easier and people can access different products in a short time. In particular, people can spend and shop quickly and easily without carrying cash in their pockets with a credit card. However, with the increase in the use of credit cards, there are also some security vulnerabilities. Fraudsters can gain unfair advantage by obtaining certain credit card information such as passwords. They can shop with someone else's credit card without permission. These transactions cause substantial financial damage to individuals and institutions. With the increase in the use of credit cards with the developing technology, such credit card fraud is also increasing rapidly. Taking precautions against credit card fraud is a very important issue in order to ensure the safety of people. For this reason, in order to ensure the security of both banks and financial institutions that provide credit card services, it is necessary to prevent credit card fraud and to detect fraud that may occur in credit cards within the scope of combating fraud. In our study, Artificial Neural Networks were used to detect credit card fraud transactions. A prediction model has been developed to detect fraud in credit card transactions with ANN. Using the Credit Card data set obtained from the Kaggle database, modeling was done with the Feed Forward Artificial Neural Network method. The aim of this study is to automatically detect abnormal behaviors made with credit cards. 98.44% success was achieved with feedforward artificial neural network.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969380","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}
This paper studies the electromechanical model of a jigsaw mechanism. The jigsaw is powered by a battery, which drives a DC motor. The rotational motion is converted into linear motion through a Scotch Yoke mechanism. The electromechanical equations are based on energy approach using the Lagrange-equation. The Lagrange function contains the energies of the model, i.e., the kinetic co-energy of the kinematic chain and the magnetic co-energy of the inductance. As regard the non-conservative elements their virtual works are written. The formulated equations of the jigsaw model allow us to examine this force-energy relationship.
{"title":"Electromechanical Model of Jigsaw","authors":"György Hegedűs, Sándor Apáti","doi":"10.58190/icat.2023.24","DOIUrl":"https://doi.org/10.58190/icat.2023.24","url":null,"abstract":"This paper studies the electromechanical model of a jigsaw mechanism. The jigsaw is powered by a battery, which drives a DC motor. The rotational motion is converted into linear motion through a Scotch Yoke mechanism. The electromechanical equations are based on energy approach using the Lagrange-equation. The Lagrange function contains the energies of the model, i.e., the kinetic co-energy of the kinematic chain and the magnetic co-energy of the inductance. As regard the non-conservative elements their virtual works are written. The formulated equations of the jigsaw model allow us to examine this force-energy relationship.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969384","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}
multi-agent system plays a crucial role in many aspects of life. This refers to its ability to provide scalable and flexible solutions to complex problems. One of these problems is the management of resources in different cities around the world. In this study, we have used the multi-agent system concepts to build competent city resources management. Because of the time and software complexity that arises from implementing the whole system, we chose to implement one city entity, the energy entity. Petri net is used to model the system mathematically. At the same time, we used CPN tools software to simulate the system behaviour in real life. The mathematical model and simulation results proved that the designed energy MAS would achieve its goals when applied in real life. In the future, we intend to complete the construction of the whole system for better resource management of all entities in the city
{"title":"Toward Competent City Management using Multi-agent System (MAS)","authors":"Alaa Odeh, Rashid Jayousi, Amjad Rutrot","doi":"10.58190/icat.2023.36","DOIUrl":"https://doi.org/10.58190/icat.2023.36","url":null,"abstract":"multi-agent system plays a crucial role in many aspects of life. This refers to its ability to provide scalable and flexible solutions to complex problems. One of these problems is the management of resources in different cities around the world. In this study, we have used the multi-agent system concepts to build competent city resources management. Because of the time and software complexity that arises from implementing the whole system, we chose to implement one city entity, the energy entity. Petri net is used to model the system mathematically. At the same time, we used CPN tools software to simulate the system behaviour in real life. The mathematical model and simulation results proved that the designed energy MAS would achieve its goals when applied in real life. In the future, we intend to complete the construction of the whole system for better resource management of all entities in the city","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135969514","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}
Fungal infections, due to their diverse manifestations and varying characteristics, present significant challenges in medical diagnosis. This study delves into applying deep-learning techniques for detecting fungal infections from microscopic fungal images. By harnessing the power of Convolutional Neural Networks (CNNs), we propose an approach that employs transfer learning to accurately classify different fungal species. The dataset comprises microscopic images of various fungal types, and to enhance model performance, we utilize data augmentation techniques. Furthermore, we aim to boost performance by fine-tuning the model's layers. Initially starting at 84.38% accuracy, our experimental results progressively reached high values of 95.35% and 97.19%. These results underscore the effectiveness of our deep learning approach in precisely identifying and classifying fungal infections. This success holds promising potential to aid medical professionals in timely and accurate diagnoses. The findings presented in this study contribute to ongoing research in medical image analysis and drive advancements in the field of automated disease detection.
{"title":"Detection of Fungal Infections from Microscopic Fungal Images Using Deep Learning Techniques","authors":"Ilkay Cinar, Yavuz Selim Taspinar","doi":"10.58190/icat.2023.12","DOIUrl":"https://doi.org/10.58190/icat.2023.12","url":null,"abstract":"Fungal infections, due to their diverse manifestations and varying characteristics, present significant challenges in medical diagnosis. This study delves into applying deep-learning techniques for detecting fungal infections from microscopic fungal images. By harnessing the power of Convolutional Neural Networks (CNNs), we propose an approach that employs transfer learning to accurately classify different fungal species. The dataset comprises microscopic images of various fungal types, and to enhance model performance, we utilize data augmentation techniques. Furthermore, we aim to boost performance by fine-tuning the model's layers. Initially starting at 84.38% accuracy, our experimental results progressively reached high values of 95.35% and 97.19%. These results underscore the effectiveness of our deep learning approach in precisely identifying and classifying fungal infections. This success holds promising potential to aid medical professionals in timely and accurate diagnoses. The findings presented in this study contribute to ongoing research in medical image analysis and drive advancements in the field of automated disease detection.","PeriodicalId":20592,"journal":{"name":"PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87539224","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}
PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES IN MATERIALS SCIENCE, MECHANICAL AND AUTOMATION ENGINEERING: MIP: Engineering-III – 2021
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：