Pub Date : 2023-06-08DOI: 10.1109/HORA58378.2023.10156750
Erkan Tur
In the plastics industry, particularly in multistage extrusion processes, maintaining a consistent product quality is paramount. The extrusion process often involves converting granular raw material into a plastic film by heating and stretching it across multiple layers. Two significant aspects of the output product quality are product parameters such as film thickness and stretch, and the presence or absence of defects. Currently, product parameters are efficiently monitored using sensors, but defect identification largely relies on the manual visual inspection by the operator, which may not always occur in real time. This manual approach is prone to errors and can result in delayed defect detection. This study proposes to explore the application of deep learning to automate defect detection in the multi-stage plastic extrusion process. By training deep learning models on a rich dataset of process parameters of the output product, it is possible to enable realtime, automatic identification of defects. This can lead to a substantial improvement in the efficiency and accuracy of the quality control process. Various deep learning architectures will be employed and evaluated for their effectiveness in this task. Furthermore, this study also aims to investigate the correlation between various factors, including equipment performance and quality of incoming raw materials, and the occurrence of defects. Advanced deep learning techniques like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks will be used to analyze the time-series data from the extrusion process. The findings from this analysis could provide valuable insights into the root causes of defects and guide efforts to minimize their occurrence. In conclusion, this research seeks to leverage the potential of deep learning to enhance the quality control process in the multi-stage plastic extrusion industry, with a focus on automated, real-time defect detection and root cause analysis.
{"title":"Applying Deep Learning for Automated Quality Control and Defect Detection in Multi-stage Plastic Extrusion Process","authors":"Erkan Tur","doi":"10.1109/HORA58378.2023.10156750","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156750","url":null,"abstract":"In the plastics industry, particularly in multistage extrusion processes, maintaining a consistent product quality is paramount. The extrusion process often involves converting granular raw material into a plastic film by heating and stretching it across multiple layers. Two significant aspects of the output product quality are product parameters such as film thickness and stretch, and the presence or absence of defects. Currently, product parameters are efficiently monitored using sensors, but defect identification largely relies on the manual visual inspection by the operator, which may not always occur in real time. This manual approach is prone to errors and can result in delayed defect detection. This study proposes to explore the application of deep learning to automate defect detection in the multi-stage plastic extrusion process. By training deep learning models on a rich dataset of process parameters of the output product, it is possible to enable realtime, automatic identification of defects. This can lead to a substantial improvement in the efficiency and accuracy of the quality control process. Various deep learning architectures will be employed and evaluated for their effectiveness in this task. Furthermore, this study also aims to investigate the correlation between various factors, including equipment performance and quality of incoming raw materials, and the occurrence of defects. Advanced deep learning techniques like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks will be used to analyze the time-series data from the extrusion process. The findings from this analysis could provide valuable insights into the root causes of defects and guide efforts to minimize their occurrence. In conclusion, this research seeks to leverage the potential of deep learning to enhance the quality control process in the multi-stage plastic extrusion industry, with a focus on automated, real-time defect detection and root cause analysis.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116406870","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156676
Yousif Samer Mudhafar, S. H. Abdulnabi
The goal of the research to design and implement digital filters (Finite Impulse Response (FIR) and Infinite Impulse Response (IIR)) based on Field Programmable Gate Array (FPGA) by using the copulation between MATLAB/Simulink and Xilinx ISE Design Suite programs. low pass digital filter was implemented with different types of windowing methods that calculate the filter coefficient of FIR filter and different types of IIR filter with three numbers of filter order that are (5th order, 8th order, and 10th order). These different types of digital filters and filter orders are applied with the addition of a sine signal with a frequency of 16 Hz and a random noise signal. The work was done by two approaches: the first by simulation method through coupling between MATLAB/Simulink and Xilinx ISE Design Suite programs. While the second is by the practical method of loading these simulation block diagrams on FPGA. The performance of the work is measured by the difference between the sine signal and filtered signal and by the difference between the simulation results and practical results. Using FPGA with digital filters in this research gives a major advantage which is the simulation results equal to the practical results (Difference equal to zero).
研究的目标是利用MATLAB/Simulink和Xilinx ISE design Suite程序之间的耦合，设计和实现基于现场可编程门阵列(FPGA)的数字滤波器(有限脉冲响应(FIR)和无限脉冲响应(IIR))。采用不同类型的开窗方法实现低通数字滤波器，分别计算FIR滤波器和不同类型的IIR滤波器的滤波器系数，滤波器阶数为5阶、8阶和10阶。这些不同类型的数字滤波器和滤波器阶数被应用于频率为16 Hz的正弦信号和随机噪声信号的添加。通过两种方法完成工作:第一种方法是通过MATLAB/Simulink与Xilinx ISE Design Suite程序之间的耦合进行仿真。而第二种是通过在FPGA上加载这些仿真方框图的实用方法。通过正弦信号与滤波信号的差值以及仿真结果与实际结果的差值来衡量工作的性能。在本研究中使用FPGA和数字滤波器的一个主要优点是仿真结果与实际结果相等(差等于零)。
{"title":"High Performance FIR and IIR Filters Based on FPGA for 16 Hz Signal Processing","authors":"Yousif Samer Mudhafar, S. H. Abdulnabi","doi":"10.1109/HORA58378.2023.10156676","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156676","url":null,"abstract":"The goal of the research to design and implement digital filters (Finite Impulse Response (FIR) and Infinite Impulse Response (IIR)) based on Field Programmable Gate Array (FPGA) by using the copulation between MATLAB/Simulink and Xilinx ISE Design Suite programs. low pass digital filter was implemented with different types of windowing methods that calculate the filter coefficient of FIR filter and different types of IIR filter with three numbers of filter order that are (5th order, 8th order, and 10th order). These different types of digital filters and filter orders are applied with the addition of a sine signal with a frequency of 16 Hz and a random noise signal. The work was done by two approaches: the first by simulation method through coupling between MATLAB/Simulink and Xilinx ISE Design Suite programs. While the second is by the practical method of loading these simulation block diagrams on FPGA. The performance of the work is measured by the difference between the sine signal and filtered signal and by the difference between the simulation results and practical results. Using FPGA with digital filters in this research gives a major advantage which is the simulation results equal to the practical results (Difference equal to zero).","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130500659","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156719
Ceren Dursun, Alper Ozcan
Ahstract-The integration of recommender systems contributes to the tourism industry as it provides tailored recommendations to users, assisting them in discovering and selecting the most suitable accommodation options based on their particular needs and preferences. By providing personalized recommendations that are tailored to each user's preferences and needs, hotel rec-ommendation systems could assist in reducing the time and effort required to find the best hotel options. In addition, users could discover new and relevant accommodation alternatives that they might not have previously considered. Despite the importance of the reasons underlying user preferences, existing review-based recommendation systems often neglect the importance of sentiment words linked to related item aspects. To address this need, this study presents a sentiment-enhanced hotel recommender system using neural collaborative filtering that incorporates information derived from both textual reviews and user-hotel relationships. This study employs a neural collaborative filtering approach to learn the relationship between user-hotel interactions and a sentiment-enhanced recommendation system. In regards to the experiment conducted in this study, our method enhances the model's ability to capture user preferences and item features through information from sentiment-enhanced text reviews in comparison to sub-ratings generated by users. Aspect-based sentiment analysis improves personalized hotel recommendations by taking into account the sentiment toward specific aspects of the hotel, such as cleanliness, service, or location.
{"title":"Sentiment-enhanced Neural Collaborative Filtering Models Using Explicit User Preferences","authors":"Ceren Dursun, Alper Ozcan","doi":"10.1109/HORA58378.2023.10156719","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156719","url":null,"abstract":"Ahstract-The integration of recommender systems contributes to the tourism industry as it provides tailored recommendations to users, assisting them in discovering and selecting the most suitable accommodation options based on their particular needs and preferences. By providing personalized recommendations that are tailored to each user's preferences and needs, hotel rec-ommendation systems could assist in reducing the time and effort required to find the best hotel options. In addition, users could discover new and relevant accommodation alternatives that they might not have previously considered. Despite the importance of the reasons underlying user preferences, existing review-based recommendation systems often neglect the importance of sentiment words linked to related item aspects. To address this need, this study presents a sentiment-enhanced hotel recommender system using neural collaborative filtering that incorporates information derived from both textual reviews and user-hotel relationships. This study employs a neural collaborative filtering approach to learn the relationship between user-hotel interactions and a sentiment-enhanced recommendation system. In regards to the experiment conducted in this study, our method enhances the model's ability to capture user preferences and item features through information from sentiment-enhanced text reviews in comparison to sub-ratings generated by users. Aspect-based sentiment analysis improves personalized hotel recommendations by taking into account the sentiment toward specific aspects of the hotel, such as cleanliness, service, or location.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117205977","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156710
Aoxin Ni, Nasser Kehtamavaz
Adaptive Dynamic Range Optimization (ADRO) is an amplification strategy which is used for hearing aids and other assistive hearing devices. To take into consideration hearing preferences of a specific user in the field, ADRO has been personalized by using maximum likelihood inverse reinforcement learning. A smartphone app is developed in this paper implementing the personalization of ADRO in real-world audio environments so that clinical studies can be carried out in the field. The developed app adjusts the comfort target parameter of ADRO by conducting paired audio comparisons in real-time to reach a personalized setting of gain values in five frequency bands. The audio processing steps taken to enable the app real-time functionality are discussed. The ADRO personalization results of the experiments carried out by using the app in different real-world environments are also presented.
{"title":"A Real- Time Smartphone App for Field Personalization of Hearing Enhancement by Adaptive Dynamic Range Optimization","authors":"Aoxin Ni, Nasser Kehtamavaz","doi":"10.1109/HORA58378.2023.10156710","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156710","url":null,"abstract":"Adaptive Dynamic Range Optimization (ADRO) is an amplification strategy which is used for hearing aids and other assistive hearing devices. To take into consideration hearing preferences of a specific user in the field, ADRO has been personalized by using maximum likelihood inverse reinforcement learning. A smartphone app is developed in this paper implementing the personalization of ADRO in real-world audio environments so that clinical studies can be carried out in the field. The developed app adjusts the comfort target parameter of ADRO by conducting paired audio comparisons in real-time to reach a personalized setting of gain values in five frequency bands. The audio processing steps taken to enable the app real-time functionality are discussed. The ADRO personalization results of the experiments carried out by using the app in different real-world environments are also presented.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463151","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156667
Nikola Hure, M. Vašak
This paper focuses on the identification of thermodynamic models for temperature prediction in households. The proposed temperature dynamics model falls under the class of Linear Time-Invariant (LTI) models, making it suitable for model predictive control synthesis. However, the presence of significant and variable thermal disturbances in households adds complexity to the identification process. The performance of various prediction error methods, such as ARX, ARARMAX, and BJ models, along with simplified models incorporating persistent disturbance excitation, is analyzed. The findings highlight the substantial impact of unknown disturbances on temperature predictions, emphasizing the crucial need for accurate prediction of these disturbances for effective household heating and cooling planning. The identification and evaluation of model performance measures are conducted using two months of experimental data collected from five households. This study contributes to understanding of the significance of addressing unknown disturbances and variability in thermodynamic model identification for temperature prediction.
{"title":"Quantitative Analysis of Regression-Based Temperature Dynamics Models for Households with A/C Units Subject to Unknown Disturbances","authors":"Nikola Hure, M. Vašak","doi":"10.1109/HORA58378.2023.10156667","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156667","url":null,"abstract":"This paper focuses on the identification of thermodynamic models for temperature prediction in households. The proposed temperature dynamics model falls under the class of Linear Time-Invariant (LTI) models, making it suitable for model predictive control synthesis. However, the presence of significant and variable thermal disturbances in households adds complexity to the identification process. The performance of various prediction error methods, such as ARX, ARARMAX, and BJ models, along with simplified models incorporating persistent disturbance excitation, is analyzed. The findings highlight the substantial impact of unknown disturbances on temperature predictions, emphasizing the crucial need for accurate prediction of these disturbances for effective household heating and cooling planning. The identification and evaluation of model performance measures are conducted using two months of experimental data collected from five households. This study contributes to understanding of the significance of addressing unknown disturbances and variability in thermodynamic model identification for temperature prediction.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122579028","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156672
Fawaz Y. Abdullah, M. T. Yaseen, Y. S. Sheet
Advanced technology could provide smart solutions to improve the efficiency of work in different fields. One of the important fields in the world is farming. However, traditional farming, especially in developing countries, faces many challenges to increase the food quality and quantity. For instance, some of these challenges are limited arable land, high overall cost, and pollution. Here, a fully automated eco-friendly, low- cost, and smart control unit was proposed and implemented to enhance the overall traditional farming efficiency. This smart control unit will provide the basis for building smart farming control system (SFCS). The main advantage of this SFCS is to manage efficiently the farm resources such as the energy and water. Solar cells panels supported with rechargeable unit were used to produce energy from clean resources and keep the surrounding environment healthy, which can reduce the pollution. Another important advantage is to reduce the waste of water usage in the farm by using an efficient water management system. In addition, other factors inside the farm were controlled within certain values such as the temperature, humidity, and emission of gases. Short-and long-range communication schemes were deployed as well. SFCS reduces the farm resources waste, labor work, emission, working time and overall cost. On the other hand, it increases the quality, quantity of production and keeps the working environment sustainable. This upgradable and flexible system would play a vital role to improve the overall farm efficiency and productivity, especially in developing countries where the food demand and population are increasing in contrast to the lack of water and energy resources.
{"title":"Smart Eco-Friendly and Low-Cost Farming Control System","authors":"Fawaz Y. Abdullah, M. T. Yaseen, Y. S. Sheet","doi":"10.1109/HORA58378.2023.10156672","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156672","url":null,"abstract":"Advanced technology could provide smart solutions to improve the efficiency of work in different fields. One of the important fields in the world is farming. However, traditional farming, especially in developing countries, faces many challenges to increase the food quality and quantity. For instance, some of these challenges are limited arable land, high overall cost, and pollution. Here, a fully automated eco-friendly, low- cost, and smart control unit was proposed and implemented to enhance the overall traditional farming efficiency. This smart control unit will provide the basis for building smart farming control system (SFCS). The main advantage of this SFCS is to manage efficiently the farm resources such as the energy and water. Solar cells panels supported with rechargeable unit were used to produce energy from clean resources and keep the surrounding environment healthy, which can reduce the pollution. Another important advantage is to reduce the waste of water usage in the farm by using an efficient water management system. In addition, other factors inside the farm were controlled within certain values such as the temperature, humidity, and emission of gases. Short-and long-range communication schemes were deployed as well. SFCS reduces the farm resources waste, labor work, emission, working time and overall cost. On the other hand, it increases the quality, quantity of production and keeps the working environment sustainable. This upgradable and flexible system would play a vital role to improve the overall farm efficiency and productivity, especially in developing countries where the food demand and population are increasing in contrast to the lack of water and energy resources.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128547534","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156671
H. A. Al-Tayyar, Y. E. Mohammed Ali
A Metamaterial (MTM) is an artificial structure with electromagnetic characteristics which are not available naturally in any other materials. This MTM gained an importance in various 5G applications as its performance improvement especially in antenna design. In this paper, a wide band MTM (−10dB bandwidth equal to 1 GHz), miniaturized size, and double negative properties has designed for millimeter wave frequencies. The proposed MTM has printed on the substrate layer of Rogers5880 with permittivity of 2.2 and loss tangent is 0.0009, operating at 28 GHz to be suitable for 5G applications. To achieve metamaterial properties, the permittivity, permeability, refractive index, and impedance have been extracted using retrieve robust method from the reflection coefficient and transmission coefficient. The simulation results revealed that the proposed MTM unit cell attains the lowest loss and double negative nature DNG at resonant frequency.
{"title":"Parameters Extraction of Miniaturized Metamaterial Unit Cell at Millimeter Wave Applications","authors":"H. A. Al-Tayyar, Y. E. Mohammed Ali","doi":"10.1109/HORA58378.2023.10156671","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156671","url":null,"abstract":"A Metamaterial (MTM) is an artificial structure with electromagnetic characteristics which are not available naturally in any other materials. This MTM gained an importance in various 5G applications as its performance improvement especially in antenna design. In this paper, a wide band MTM (−10dB bandwidth equal to 1 GHz), miniaturized size, and double negative properties has designed for millimeter wave frequencies. The proposed MTM has printed on the substrate layer of Rogers5880 with permittivity of 2.2 and loss tangent is 0.0009, operating at 28 GHz to be suitable for 5G applications. To achieve metamaterial properties, the permittivity, permeability, refractive index, and impedance have been extracted using retrieve robust method from the reflection coefficient and transmission coefficient. The simulation results revealed that the proposed MTM unit cell attains the lowest loss and double negative nature DNG at resonant frequency.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115906672","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156730
Ahmed Raed Sabah Alrashed, Timur İnan
The fundamental purpose of the field of research known as “biometrics” is to explore the development of reliable approaches for identifying individuals using their observable traits. Examples of biometric identification include both physical and mental traits of an individual. As a kind of physical identification, fingerprints and facial features may be compared and analyzed. Human gait has been researched because it has the potential to be used as a behavioral identifier in computer visionUsing a Convolutional Neural Network (CNN) and a Support Vector Machine, the capacity to estimate a person's gender based on their stride was explored and investigated (SVM). Throughout our examination of CNN's potential uses for gait-based gender identification, we will strive for both a high degree of accuracy and a cheap computational cost. We analyzed and experimented with a variety of CNN architectures and hyperparameters in this setting.
{"title":"Age And Gender Detection By Face Segmentation And Modefied CNN Algorithm","authors":"Ahmed Raed Sabah Alrashed, Timur İnan","doi":"10.1109/HORA58378.2023.10156730","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156730","url":null,"abstract":"The fundamental purpose of the field of research known as “biometrics” is to explore the development of reliable approaches for identifying individuals using their observable traits. Examples of biometric identification include both physical and mental traits of an individual. As a kind of physical identification, fingerprints and facial features may be compared and analyzed. Human gait has been researched because it has the potential to be used as a behavioral identifier in computer visionUsing a Convolutional Neural Network (CNN) and a Support Vector Machine, the capacity to estimate a person's gender based on their stride was explored and investigated (SVM). Throughout our examination of CNN's potential uses for gait-based gender identification, we will strive for both a high degree of accuracy and a cheap computational cost. We analyzed and experimented with a variety of CNN architectures and hyperparameters in this setting.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115441458","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10155775
Baraa Taha Yaseen
Support vector machine (SVM), a classifier based on machine learning, has also been utilized. The training and evaluation of machine learning was conducted using data from a drug bank. The absence of negative DTI to train on is the greatest obstacle in using machine learning for this purpose. Despite the vast disparity in computing power, the support vector machine (SVM) obtained a superior area under the ROC curve (AUC) of 0.753 0.006 to the most advanced network-based method's 0.886 0.010. After extensive testing, we determined that SVM provided the maximum level of accuracy, 93.76 percent. This was unexpected and may indicate the existence of previously unknown DDI varieties or the maturation of scientific methodologies for studying DDIs. It could be used to characterize several DDI types that were not discovered until advanced processing methods or instruments, such as high-throughput screening, were developed.
{"title":"Drug Target Interaction Prediction Using Support Vector Machine (SVM)","authors":"Baraa Taha Yaseen","doi":"10.1109/HORA58378.2023.10155775","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10155775","url":null,"abstract":"Support vector machine (SVM), a classifier based on machine learning, has also been utilized. The training and evaluation of machine learning was conducted using data from a drug bank. The absence of negative DTI to train on is the greatest obstacle in using machine learning for this purpose. Despite the vast disparity in computing power, the support vector machine (SVM) obtained a superior area under the ROC curve (AUC) of 0.753 0.006 to the most advanced network-based method's 0.886 0.010. After extensive testing, we determined that SVM provided the maximum level of accuracy, 93.76 percent. This was unexpected and may indicate the existence of previously unknown DDI varieties or the maturation of scientific methodologies for studying DDIs. It could be used to characterize several DDI types that were not discovered until advanced processing methods or instruments, such as high-throughput screening, were developed.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115805103","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 : 2023-06-08DOI: 10.1109/HORA58378.2023.10156787
Sergiy Shevchenko
The paper proposes an approach to forming solutions to multi-criteria selection problems from the standpoint of their ordering by a number of agreed criteria. Existing examples of solving such problems in some cases are on the based use of a number of assumptions that are not in practice fulfilled. The paper proposes to optimize solutions to multi-criteria selection problems by forming a Pareto-optimal subset of solutions with ordering of its elements by the level of approximation to the constructed virtual variant with the best values of the selected criteria and the amount of resources used. Comparison of the candidates for selection is on comparisons of values according to agreed criteria based, the definition of which is by a set of mathematical models that reproduce the dependences of the estimates of the values of the selected criteria on the attributes of the candidates provided. An example of optimizing the construction of a virtual data processing system is using virtual computers from providers of cloud processing services and technologies presented. The results obtained indicate the possibility of using the proposed approach as part of decision support subsystems to solve the problems of operational management of dynamic service and production processes.
{"title":"Optimization of Multi-Criteria Solutions to Selection Problems","authors":"Sergiy Shevchenko","doi":"10.1109/HORA58378.2023.10156787","DOIUrl":"https://doi.org/10.1109/HORA58378.2023.10156787","url":null,"abstract":"The paper proposes an approach to forming solutions to multi-criteria selection problems from the standpoint of their ordering by a number of agreed criteria. Existing examples of solving such problems in some cases are on the based use of a number of assumptions that are not in practice fulfilled. The paper proposes to optimize solutions to multi-criteria selection problems by forming a Pareto-optimal subset of solutions with ordering of its elements by the level of approximation to the constructed virtual variant with the best values of the selected criteria and the amount of resources used. Comparison of the candidates for selection is on comparisons of values according to agreed criteria based, the definition of which is by a set of mathematical models that reproduce the dependences of the estimates of the values of the selected criteria on the attributes of the candidates provided. An example of optimizing the construction of a virtual data processing system is using virtual computers from providers of cloud processing services and technologies presented. The results obtained indicate the possibility of using the proposed approach as part of decision support subsystems to solve the problems of operational management of dynamic service and production processes.","PeriodicalId":247679,"journal":{"name":"2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126964358","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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