Pub Date : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865630
S. T, S. Gandhi
Extensive Massive MIMO (Multiple Input, Multiple Output) has the potential to satisfy the high data rate needs of future 6G. In order to get effective precoding, accurate information about the channel's current condition is critical. Existing Low pilot overhead estimate approaches strongly depend on attained angular domain channel sparsity based on plane wave front in the far field (FF) and polar domain channel sparsity based on spherical wave front in the near field (NF). In practical scenario some scatterers are present in FF region and some are present in NF. However FF as well as NF schemes for estimating the channel is not enough to find out the combined field Extensive Massive MIMO channel. An effective combined field approach for predicting the Extensive Massive MIMO channel is proposed in order to overcome this difficulty. Results from simulations reveal that the suggested approach is more efficient than the current one.
{"title":"Channel Estimation for Compound Field Extensive Massive MIMO","authors":"S. T, S. Gandhi","doi":"10.1109/CyberneticsCom55287.2022.9865630","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865630","url":null,"abstract":"Extensive Massive MIMO (Multiple Input, Multiple Output) has the potential to satisfy the high data rate needs of future 6G. In order to get effective precoding, accurate information about the channel's current condition is critical. Existing Low pilot overhead estimate approaches strongly depend on attained angular domain channel sparsity based on plane wave front in the far field (FF) and polar domain channel sparsity based on spherical wave front in the near field (NF). In practical scenario some scatterers are present in FF region and some are present in NF. However FF as well as NF schemes for estimating the channel is not enough to find out the combined field Extensive Massive MIMO channel. An effective combined field approach for predicting the Extensive Massive MIMO channel is proposed in order to overcome this difficulty. Results from simulations reveal that the suggested approach is more efficient than the current one.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124686761","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865381
Borirak Moonphala, Aphirak Jansang, Withawat Tangtrongpairoj, C. Jaikaeo, A. Phonphoem
For live video streaming in areas with unexpected events, such as accidents or protests, the network traffic can dramatically increase beyond the network capacity. All the live streams in the same area become broken, or video quality becomes jittering. Therefore, this paper proposed the LTE radio resource management for live video streaming in dense area. The system will allocate the reserved resource block for priority UE using the concept of prioritization and the Chanel Quality Indicator (CQI) value. The simulation experiment investigated the appropriate reserved block size of priority UE considering the effect on the non priority UE. The results show that priority UE's throughput and inter packet arrival time meet the required criteria with less impact on the non priority UE.
{"title":"LTE Network Resource Management for Live Video Streaming in Dense Area","authors":"Borirak Moonphala, Aphirak Jansang, Withawat Tangtrongpairoj, C. Jaikaeo, A. Phonphoem","doi":"10.1109/CyberneticsCom55287.2022.9865381","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865381","url":null,"abstract":"For live video streaming in areas with unexpected events, such as accidents or protests, the network traffic can dramatically increase beyond the network capacity. All the live streams in the same area become broken, or video quality becomes jittering. Therefore, this paper proposed the LTE radio resource management for live video streaming in dense area. The system will allocate the reserved resource block for priority UE using the concept of prioritization and the Chanel Quality Indicator (CQI) value. The simulation experiment investigated the appropriate reserved block size of priority UE considering the effect on the non priority UE. The results show that priority UE's throughput and inter packet arrival time meet the required criteria with less impact on the non priority UE.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122587324","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865647
I. G. A. Premananda, A. Tjahyanto, A. Muklason
Timetabling problem at universities is one of the problems that require more attention in operations research. This problem is known as NP-Hard problem, therefore non-deterministic exact algorithm could solve problems within this category within polynomial time. The heuristic approach can produce a fairly good solution within polynomial time but does not guarantee that the solution is optimal. So, there is always a gap in a heuristic algorithm that can be studied to result enhanced algorithm with better performance. There are a lot of timetabling problem domains in the literature that have been well studied in the scientific literature especially in the field of operational research and artificial intelligence. However, there are still few prior studies reported in the literature that focus on solving relatively new timetabling problem domain of International Timetabling Competition 2019 (ITC 2019). The competition presents real-world datasets with high complexity and large problem sizes. This paper reports our study of developing a novel algorithm called the Hybrid Whale Optimization Algorithm to solve the ITC 2019 problem. The algorithm combines the adapted whale optimization algorithm (WOA) and Late Acceptance Hill Climbing (LAHC) algorithm. The experimental results show that The WOA algorithm successfully improved the average penalty value by 65%. Furthermore, the hybrid WOA improves the WOA algorithm even better, especially on four datasets by 16-43%. Compared to other algorithms reported in the competition, the Hybrid WOA algorithm is ranked 7 out of 13.
{"title":"Hybrid Whale Optimization Algorithm for Solving Timetabling Problems of ITC 2019","authors":"I. G. A. Premananda, A. Tjahyanto, A. Muklason","doi":"10.1109/CyberneticsCom55287.2022.9865647","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865647","url":null,"abstract":"Timetabling problem at universities is one of the problems that require more attention in operations research. This problem is known as NP-Hard problem, therefore non-deterministic exact algorithm could solve problems within this category within polynomial time. The heuristic approach can produce a fairly good solution within polynomial time but does not guarantee that the solution is optimal. So, there is always a gap in a heuristic algorithm that can be studied to result enhanced algorithm with better performance. There are a lot of timetabling problem domains in the literature that have been well studied in the scientific literature especially in the field of operational research and artificial intelligence. However, there are still few prior studies reported in the literature that focus on solving relatively new timetabling problem domain of International Timetabling Competition 2019 (ITC 2019). The competition presents real-world datasets with high complexity and large problem sizes. This paper reports our study of developing a novel algorithm called the Hybrid Whale Optimization Algorithm to solve the ITC 2019 problem. The algorithm combines the adapted whale optimization algorithm (WOA) and Late Acceptance Hill Climbing (LAHC) algorithm. The experimental results show that The WOA algorithm successfully improved the average penalty value by 65%. Furthermore, the hybrid WOA improves the WOA algorithm even better, especially on four datasets by 16-43%. Compared to other algorithms reported in the competition, the Hybrid WOA algorithm is ranked 7 out of 13.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114584456","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865284
Riswandi, M. Niswar, Z. Tahir, Zainal, Chong Yung Wey
Urban farming becomes popular and enhances food security in urban areas. Aeroponic is one of the urban farming methods where plant roots are suspended in the air and are supplied with nutrients by mist spraying. This paper describes an IoT system for monitoring and controlling evapotranspiration in an aeroponic environment. The system consists of a microcontroller, single-board computer, sensors, and actuators. The sensors in the system collect the data on plant environment parameters including air temperature, humidity, total dissolved solids (TDS), pH, and water temperature. Then, the system calculates the level of evapotranspiration using the Blaney-Criddle method to determine the appropriate actuator action to reduce the level of evapotranspiration in aeroponic using a fuzzy algorithm. The experimental result shows that our IoT system can reduce evapotranspiration, hence, it can improve the plant quality.
{"title":"Design and Implementation of IoT-Based Aeroponic Farming System","authors":"Riswandi, M. Niswar, Z. Tahir, Zainal, Chong Yung Wey","doi":"10.1109/CyberneticsCom55287.2022.9865284","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865284","url":null,"abstract":"Urban farming becomes popular and enhances food security in urban areas. Aeroponic is one of the urban farming methods where plant roots are suspended in the air and are supplied with nutrients by mist spraying. This paper describes an IoT system for monitoring and controlling evapotranspiration in an aeroponic environment. The system consists of a microcontroller, single-board computer, sensors, and actuators. The sensors in the system collect the data on plant environment parameters including air temperature, humidity, total dissolved solids (TDS), pH, and water temperature. Then, the system calculates the level of evapotranspiration using the Blaney-Criddle method to determine the appropriate actuator action to reduce the level of evapotranspiration in aeroponic using a fuzzy algorithm. The experimental result shows that our IoT system can reduce evapotranspiration, hence, it can improve the plant quality.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129197360","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865559
B. Pratomo, Ahmad Ibnu Fajar, Abdul Munif, R. Ijtihadie, H. Studiawan, B. J. Santoso
A Network-based Intrusion Detection System (NIDS) monitors network traffic and analyses it to look for any sign of malicious behaviour. A NIDS may be using of these two methods to look for malicious activities, signature-based or anomaly-based. A Signature-based NIDS relies on a database of rulesets to determine whether a packet or a flow is malicious. Therefore, it suffers when the database is not updated regularly or when a zero-day attack appears. An Anomaly-based NIDS works by learning the behaviour of normal traffic and looking for anomalous activities. The anomalous activities are then deemed malicious. In doing so, this kind of NIDS does not have to rely on an updated database. It can identify deviation from the normal behaviour by training itself with some training data obtained from the organisation network traffic. The issue is cleaning the network traffic data from a real-world capture is time-consuming. Thus, in this paper, we proposed an anomaly detection method that was trained with network traffic that contains malicious activities. We were looking for evidence of whether using Autoencoders is robust to noisy data in the training set. Our experiments show that the detection method can achieve an F2-score of 0.87 for FTP traffic, 0.83 for HTTP traffic, and 0.98 for SMTP traffic. These results were obtained from models that had been trained with a training set which contains 0.3% of malicious traffic.
{"title":"Training Autoencoders with Noisy Training Sets for Detecting Low-rate Attacks on the Network","authors":"B. Pratomo, Ahmad Ibnu Fajar, Abdul Munif, R. Ijtihadie, H. Studiawan, B. J. Santoso","doi":"10.1109/CyberneticsCom55287.2022.9865559","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865559","url":null,"abstract":"A Network-based Intrusion Detection System (NIDS) monitors network traffic and analyses it to look for any sign of malicious behaviour. A NIDS may be using of these two methods to look for malicious activities, signature-based or anomaly-based. A Signature-based NIDS relies on a database of rulesets to determine whether a packet or a flow is malicious. Therefore, it suffers when the database is not updated regularly or when a zero-day attack appears. An Anomaly-based NIDS works by learning the behaviour of normal traffic and looking for anomalous activities. The anomalous activities are then deemed malicious. In doing so, this kind of NIDS does not have to rely on an updated database. It can identify deviation from the normal behaviour by training itself with some training data obtained from the organisation network traffic. The issue is cleaning the network traffic data from a real-world capture is time-consuming. Thus, in this paper, we proposed an anomaly detection method that was trained with network traffic that contains malicious activities. We were looking for evidence of whether using Autoencoders is robust to noisy data in the training set. Our experiments show that the detection method can achieve an F2-score of 0.87 for FTP traffic, 0.83 for HTTP traffic, and 0.98 for SMTP traffic. These results were obtained from models that had been trained with a training set which contains 0.3% of malicious traffic.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129216027","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865507
A. M. A. A. Mangkona, Faizal Arya Samman, Syafaruddin
This paper presents Silicon Controlled Rectifier (SCR) based power flow control for grid integration in home-scale photovoltaic system. Power flow control is used to control the power flow from the grid if the power from solar panel and battery cannot meet the power requirements of the load. To control the power on the grid, a full-wave SCR circuit is designed. The full-wave SCR circuit serves to convert 220 Volt AC from the grid into 36 Volt DC. The effect of the delay angle of the SCR on the full-wave SCR output voltage is shown in this paper. A filter with a combination of an inductor (L) and a capacitor (C) which is assembled into an LCLC filter in parallel with the load aims to smooth out ripples in the output voltage. The circuit is designed and modeled in The PSpice program code, then simulated and analyzed. The simulation and implementation test results show that the voltage can work at the setpoint voltage at 100-ohm and 50-ohm loads. The full-wave SCR circuit can supply up to 1.8kW based on the simulation results.
{"title":"SCR-Based Power Flow Control for Grid Integration in Home-Scale Photovoltaic System","authors":"A. M. A. A. Mangkona, Faizal Arya Samman, Syafaruddin","doi":"10.1109/CyberneticsCom55287.2022.9865507","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865507","url":null,"abstract":"This paper presents Silicon Controlled Rectifier (SCR) based power flow control for grid integration in home-scale photovoltaic system. Power flow control is used to control the power flow from the grid if the power from solar panel and battery cannot meet the power requirements of the load. To control the power on the grid, a full-wave SCR circuit is designed. The full-wave SCR circuit serves to convert 220 Volt AC from the grid into 36 Volt DC. The effect of the delay angle of the SCR on the full-wave SCR output voltage is shown in this paper. A filter with a combination of an inductor (L) and a capacitor (C) which is assembled into an LCLC filter in parallel with the load aims to smooth out ripples in the output voltage. The circuit is designed and modeled in The PSpice program code, then simulated and analyzed. The simulation and implementation test results show that the voltage can work at the setpoint voltage at 100-ohm and 50-ohm loads. The full-wave SCR circuit can supply up to 1.8kW based on the simulation results.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114826151","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865297
Md. Faiyed Bin Karim, Tasnimul Hasan, Nushera Tazreen, Safayat Bin Hakim, Samiha Tarannum
In today's digital age, one of the predominant causes of the security breaches is phishing web sites that disguise them-selves as legitimate web sites and trick unsuspecting users into revealing sensitive information. With the proliferation of high-speed internet and the popularization of IT education, there is an increase in unscrupulous actors on the web who are always ready to counterfeit a legitimate website and use it to deceive and ma-nipulate users. Software and non-software-based techniques have been used to try to unmask the phishers. Phishing web sites have many characteristics in them. Thus, classifying and detecting those is unavoidably time-consuming and complex. Our research analyzed several hybrid machine learning models, including a bespoke preprocessing step of reducing minimally correlated features and then training with four boosting algorithms and three SVM models for classification. These models have also been trained after hyperparameter tuning. Among the investigated models, XGBoost brought the highest accuracy of 97.0455% after the hyperparameter tuning.
{"title":"An investigation of ML techniques to detect Phishing Websites by complexity reduction","authors":"Md. Faiyed Bin Karim, Tasnimul Hasan, Nushera Tazreen, Safayat Bin Hakim, Samiha Tarannum","doi":"10.1109/CyberneticsCom55287.2022.9865297","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865297","url":null,"abstract":"In today's digital age, one of the predominant causes of the security breaches is phishing web sites that disguise them-selves as legitimate web sites and trick unsuspecting users into revealing sensitive information. With the proliferation of high-speed internet and the popularization of IT education, there is an increase in unscrupulous actors on the web who are always ready to counterfeit a legitimate website and use it to deceive and ma-nipulate users. Software and non-software-based techniques have been used to try to unmask the phishers. Phishing web sites have many characteristics in them. Thus, classifying and detecting those is unavoidably time-consuming and complex. Our research analyzed several hybrid machine learning models, including a bespoke preprocessing step of reducing minimally correlated features and then training with four boosting algorithms and three SVM models for classification. These models have also been trained after hyperparameter tuning. Among the investigated models, XGBoost brought the highest accuracy of 97.0455% after the hyperparameter tuning.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"44 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132974338","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865308
Iqbal Kurniawan Asmar Putra, Muhammad Ainul Fikri, Syukron Abu Ishaq Alfarozi, S. Wibirama
Eye tracking is used to observe where users are looking, how long they are looking, and what order they are looking. Eye tracking has been widely used in various fields such as helping people with disabilities by using Electro-Oculography (EOG) and analyzing eye movements signal in vestibular patients by using Video-Oculography (VOG). The human eye has a cornea and retina that are located between the front and back of the human eye. Eye movement signal analysis is a necessary step prior to eye movement classification. Selecting a model and tuning the feature extraction algorithm on eye movements are tasks that researchers continue to optimize. However, there are very few studies investigating various feature extraction methods in VOG and EOG signals. To solve this research gap, this paper systematically describes feature extraction that is suitable for use in VOG and EOG signal analysis. Three main factors are important to be considered when choosing a feature extraction method: (1) classification, (2) filters and amplifiers, and (3) dataset characteristics. The results of this literature review can be used as a reference for developing feature extraction algorithms for EOG and VOG applications.
{"title":"Review of Feature Extraction on Video-Oculography (VOG) and Electro-Oculography (EOG) Signals","authors":"Iqbal Kurniawan Asmar Putra, Muhammad Ainul Fikri, Syukron Abu Ishaq Alfarozi, S. Wibirama","doi":"10.1109/CyberneticsCom55287.2022.9865308","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865308","url":null,"abstract":"Eye tracking is used to observe where users are looking, how long they are looking, and what order they are looking. Eye tracking has been widely used in various fields such as helping people with disabilities by using Electro-Oculography (EOG) and analyzing eye movements signal in vestibular patients by using Video-Oculography (VOG). The human eye has a cornea and retina that are located between the front and back of the human eye. Eye movement signal analysis is a necessary step prior to eye movement classification. Selecting a model and tuning the feature extraction algorithm on eye movements are tasks that researchers continue to optimize. However, there are very few studies investigating various feature extraction methods in VOG and EOG signals. To solve this research gap, this paper systematically describes feature extraction that is suitable for use in VOG and EOG signal analysis. Three main factors are important to be considered when choosing a feature extraction method: (1) classification, (2) filters and amplifiers, and (3) dataset characteristics. The results of this literature review can be used as a reference for developing feature extraction algorithms for EOG and VOG applications.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131227948","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865435
I. Irvanizam, Mega Siraturahmi, Aisyah Muthmainnah Binti Irwansyah, P. F. Nasir, Z. Zulfan, Nany Salwa
The office of social affairs has been helping poor or marginalized families through social aid in dealing with poverty. This social aid emphasizes decreasing the poverty rate and economic recovery affected by the COVID-19 pandemic. However, the selection process in the previous year evaluated many families as aid recipient candidates through a conventional process selected by an officer. It would be vulnerable to subjectivity in assessment. Therefore, we attempted to develop a hybrid Multiple Criteria Decision-Making (MCDM) methodology to apply it to this particular decision-making process. This methodology amalgamated the DEMATEL method and the EDAS method with different tasks. Firstly, the DEMATEL method decided the feasible criteria weights automatically based on the proficient decision-maker assessment in estimating a pairwise criteria comparison. Secondly, the EDAS applied the criteria weight values to determine alternatives rank order based on the value of the appraisal score. After comparing with the VIKOR method, the hybrid DEMATEL-EDAS method demonstrated the stability and capability in dealing with the different influence parameters on the final results.
{"title":"A Hybrid DEMATEL-EDAS Based on Multi-Criteria Decision-Making for A Social Aid Distribution Problem","authors":"I. Irvanizam, Mega Siraturahmi, Aisyah Muthmainnah Binti Irwansyah, P. F. Nasir, Z. Zulfan, Nany Salwa","doi":"10.1109/CyberneticsCom55287.2022.9865435","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865435","url":null,"abstract":"The office of social affairs has been helping poor or marginalized families through social aid in dealing with poverty. This social aid emphasizes decreasing the poverty rate and economic recovery affected by the COVID-19 pandemic. However, the selection process in the previous year evaluated many families as aid recipient candidates through a conventional process selected by an officer. It would be vulnerable to subjectivity in assessment. Therefore, we attempted to develop a hybrid Multiple Criteria Decision-Making (MCDM) methodology to apply it to this particular decision-making process. This methodology amalgamated the DEMATEL method and the EDAS method with different tasks. Firstly, the DEMATEL method decided the feasible criteria weights automatically based on the proficient decision-maker assessment in estimating a pairwise criteria comparison. Secondly, the EDAS applied the criteria weight values to determine alternatives rank order based on the value of the appraisal score. After comparing with the VIKOR method, the hybrid DEMATEL-EDAS method demonstrated the stability and capability in dealing with the different influence parameters on the final results.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132649667","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 : 2022-06-16DOI: 10.1109/CyberneticsCom55287.2022.9865591
Raghavendra Chaudhary, Arun Kumar
Cataracts are one of the most prevalent visual diseases that people get as they gets older. A cataract is a fog that forms on the lenses of our eyes. The main symptoms of this illness include dim view, colorless, and difficulties in watching a daylight. Slit lamps and fundus cameras are routinely used to detect cataracts, although they are both expensive and require domain knowledge. As a result, the shortage of skilled ophthalmologists may cause cataract identification to be delayed, necessitating medical treatment. Consequently, early detection and prohibition of cataracts might assist to reduce the frequency of occurrence of blindness. Hence the goal of this study is to utilize Convolutional Neural Networks (CNN) to diagnose cataract pathology using a publicly available Digital Camera Image dataset. The CNN cycle takes a considerable amount of time and expense. As a result, optimization will take place. It can increase accuracy while also reducing processing time. In this study the proposed model consist of three Convolutional layers, three pooling layers, one flatten layer, and two dense layers with an ADAM optimizer. The proposed CNN model can detect cataracts with a testing accuracy of 0.9925 with a loss of 0.0475, and a training accuracy of 0.9980 with loss of 0.0038, for the selected Digital Camera Images Dataset.
{"title":"Cataract Detection using Deep Learning Model on Digital Camera Images","authors":"Raghavendra Chaudhary, Arun Kumar","doi":"10.1109/CyberneticsCom55287.2022.9865591","DOIUrl":"https://doi.org/10.1109/CyberneticsCom55287.2022.9865591","url":null,"abstract":"Cataracts are one of the most prevalent visual diseases that people get as they gets older. A cataract is a fog that forms on the lenses of our eyes. The main symptoms of this illness include dim view, colorless, and difficulties in watching a daylight. Slit lamps and fundus cameras are routinely used to detect cataracts, although they are both expensive and require domain knowledge. As a result, the shortage of skilled ophthalmologists may cause cataract identification to be delayed, necessitating medical treatment. Consequently, early detection and prohibition of cataracts might assist to reduce the frequency of occurrence of blindness. Hence the goal of this study is to utilize Convolutional Neural Networks (CNN) to diagnose cataract pathology using a publicly available Digital Camera Image dataset. The CNN cycle takes a considerable amount of time and expense. As a result, optimization will take place. It can increase accuracy while also reducing processing time. In this study the proposed model consist of three Convolutional layers, three pooling layers, one flatten layer, and two dense layers with an ADAM optimizer. The proposed CNN model can detect cataracts with a testing accuracy of 0.9925 with a loss of 0.0475, and a training accuracy of 0.9980 with loss of 0.0038, for the selected Digital Camera Images Dataset.","PeriodicalId":178279,"journal":{"name":"2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124333831","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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