Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085008
Felipe Inigo Xavier Reyes Gan, G. Mayuga, Luke Bennett King Lao, E. Magsino
Tailored to address the lack of communications systems between bus and bus stops, this paper explores the use of IEEE 802.11p, a form of Dedicated Short Range Communication (DSRC), for communication in a Bus Rapid Transit (BRT) system using MATLAB through modification of an existing application Vehicle Ad-Hoc Network (VANET) Toolbox. Adding a bus mobility model and a bus entity, a Bus-Following model (BFM) is employed within a single lane setup following the BRT system. Multiple test simulations were conducted, varying in number of buses, bus stops, and information messages. Graphical representations of the communications as well as the generated information messages show that the system works according to the intended design. The simulator was also tested to the scale of a major Philippine road using the latest available passenger inflow and outflow data. The results show that communications between bus and bus stops were within acceptable parameters for latency and Packet Delivery Ratio while also showing that a bus to bus stop ratio of 2:1 was not sufficient to handle the passenger flow of EDSA during the 7AM rush hour. Overall, a verified simulator within the open-source VANET Toolbox is presented.
为了解决公交和公交站点之间缺乏通信系统的问题，本文通过修改现有的应用程序车辆自组织网络(VANET)工具箱，探讨了使用IEEE 802.11p(专用短距离通信(DSRC)的一种形式)在快速公交(BRT)系统中使用MATLAB进行通信。添加公交机动性模型和公交实体，公交跟随模型(bus - following model, BFM)在BRT系统的单车道设置中使用。进行了多次测试模拟，在公共汽车、公共汽车站和信息消息的数量上有所不同。通信的图形表示以及生成的信息消息表明系统按照预期的设计工作。模拟器还使用最新可用的乘客流入和流出数据，以菲律宾一条主要道路的规模进行了测试。结果表明，公交与公交站点之间的通信在延迟和分组交付率的可接受参数范围内，同时也表明，在上午7点高峰时段，公交与公交站点的比例为2:1不足以处理EDSA的客流。总之，在开源VANET工具箱中提出了一个经过验证的模拟器。
{"title":"An Implementation of A Bus Following Model in an 802.11p Based Simulator in MATLAB","authors":"Felipe Inigo Xavier Reyes Gan, G. Mayuga, Luke Bennett King Lao, E. Magsino","doi":"10.1109/ICAISC56366.2023.10085008","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085008","url":null,"abstract":"Tailored to address the lack of communications systems between bus and bus stops, this paper explores the use of IEEE 802.11p, a form of Dedicated Short Range Communication (DSRC), for communication in a Bus Rapid Transit (BRT) system using MATLAB through modification of an existing application Vehicle Ad-Hoc Network (VANET) Toolbox. Adding a bus mobility model and a bus entity, a Bus-Following model (BFM) is employed within a single lane setup following the BRT system. Multiple test simulations were conducted, varying in number of buses, bus stops, and information messages. Graphical representations of the communications as well as the generated information messages show that the system works according to the intended design. The simulator was also tested to the scale of a major Philippine road using the latest available passenger inflow and outflow data. The results show that communications between bus and bus stops were within acceptable parameters for latency and Packet Delivery Ratio while also showing that a bus to bus stop ratio of 2:1 was not sufficient to handle the passenger flow of EDSA during the 7AM rush hour. Overall, a verified simulator within the open-source VANET Toolbox is presented.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121699691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085308
Michele B. Freitas, V. M. Araujo, J. Magalhães
With the full application of the General Data Protection Regulation (GDPR) in the EU on 25 May 2018, data protection by design and by default become a legal obligation. The GDPR requires organizations to adapt how they handle and protect personal and sensitive data. Explicit consent for data collection and processing, report security problems affecting personal data and the appointment of a data controller (DPO) has become mandatory and is already being complied with. However, issues like security by default and by design, from a practical perspective, are still taking the first steps. In this paper we propose a process to support the software development with the essential requirements for obtaining protection and privacy in personal data. The encompasses six procedures, aligned with the SDLC cycle. Each procedure is composed of activities and reference documents. By adopting a process like we propose, organizations achieve greater compliance between the software and the GDPR, contributing to the personal data protection, as well as, the reduction of potential fines and protection against possible financial and trust/reputation losses.
{"title":"Process SDLC-GDPR: Towards the Development of Secure and Compliant Applications","authors":"Michele B. Freitas, V. M. Araujo, J. Magalhães","doi":"10.1109/ICAISC56366.2023.10085308","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085308","url":null,"abstract":"With the full application of the General Data Protection Regulation (GDPR) in the EU on 25 May 2018, data protection by design and by default become a legal obligation. The GDPR requires organizations to adapt how they handle and protect personal and sensitive data. Explicit consent for data collection and processing, report security problems affecting personal data and the appointment of a data controller (DPO) has become mandatory and is already being complied with. However, issues like security by default and by design, from a practical perspective, are still taking the first steps. In this paper we propose a process to support the software development with the essential requirements for obtaining protection and privacy in personal data. The encompasses six procedures, aligned with the SDLC cycle. Each procedure is composed of activities and reference documents. By adopting a process like we propose, organizations achieve greater compliance between the software and the GDPR, contributing to the personal data protection, as well as, the reduction of potential fines and protection against possible financial and trust/reputation losses.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130217727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085152
Ashish Sharma, Piyansh Agrawal, Krishan, B. Sharma, I. Dhaou
The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.
{"title":"Accidental Face Recognition and Detection Using Machine Learning","authors":"Ashish Sharma, Piyansh Agrawal, Krishan, B. Sharma, I. Dhaou","doi":"10.1109/ICAISC56366.2023.10085152","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085152","url":null,"abstract":"The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129075741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085242
Afnan Alyoucef, S. Qaisar, Meriem Hafsi
The generalization of the use of electronic systems and their integration in industrial systems and different aspects of modern life (internet of things, electric vehicles, robotics, smart grids), give rise to new challenges related to the storage and optimized management of energy. Lithium-on batteries perfectly meet this objective due to their good qualities such as high energy density, small installation size, low self-discharge and high supply capacity. However, their wide application requires further research on battery failure prediction and health management. Intelligent “battery management systems” (BMSs) employ the real-time estimation and control algorithms to improve the battery safety while enhancing its performance. Nevertheless, BMS are complex and require increased processing power which could lead to more power consumption. In this context, the present article provides a new approach for efficient prediction of the “Lithiumion” (Li-ion) battery cells capacities by analysing and exploiting the battery parameters, acquired by an event-driven module. It acquires the intended cells voltages, currents and temperature values during the charge-discharge cycles. The solution is based on the machine learning algorithms and event-based segmentation. The “National Aeronautics and Space Administration” (NASA) has provided a high-power Li-Ion cells dataset for the purpose of research and innovation. This dataset is used to test and evaluate the suggested approach. The evaluation of the overall performance of the chain has shown encouraging results of the proposed approach.
{"title":"Rechargeable Battery State Estimation Based on Adaptive-Rate Processing and Machine Learning","authors":"Afnan Alyoucef, S. Qaisar, Meriem Hafsi","doi":"10.1109/ICAISC56366.2023.10085242","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085242","url":null,"abstract":"The generalization of the use of electronic systems and their integration in industrial systems and different aspects of modern life (internet of things, electric vehicles, robotics, smart grids), give rise to new challenges related to the storage and optimized management of energy. Lithium-on batteries perfectly meet this objective due to their good qualities such as high energy density, small installation size, low self-discharge and high supply capacity. However, their wide application requires further research on battery failure prediction and health management. Intelligent “battery management systems” (BMSs) employ the real-time estimation and control algorithms to improve the battery safety while enhancing its performance. Nevertheless, BMS are complex and require increased processing power which could lead to more power consumption. In this context, the present article provides a new approach for efficient prediction of the “Lithiumion” (Li-ion) battery cells capacities by analysing and exploiting the battery parameters, acquired by an event-driven module. It acquires the intended cells voltages, currents and temperature values during the charge-discharge cycles. The solution is based on the machine learning algorithms and event-based segmentation. The “National Aeronautics and Space Administration” (NASA) has provided a high-power Li-Ion cells dataset for the purpose of research and innovation. This dataset is used to test and evaluate the suggested approach. The evaluation of the overall performance of the chain has shown encouraging results of the proposed approach.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129745621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085504
Saptarshi Das, Ahmed Alzimami
This paper uses the Bayesian optimization for fitting Ensemble regression models for tuning the machine learning model hyperparameters with reduced computation. We use the Pune Smart City air quality monitoring dataset with temporal variation of hazardous chemical pollutants in the air. The aim here is to reliably predict the suspended particulates as the air quality metrics using other environmental variables, considering linear models and nonlinear ensemble of tree models. To achieve good predictive accuracy a computationally expensive optimization method is required which has been achieved using the Gaussian Process surrogate assisted Bayesian optimization. We also show the diagnostics plots of the residuals from the nonlinear models to explain model quality.
{"title":"Bayesian Optimization based Hyperparameter Tuning of Ensemble Regression Models in Smart City Air Quality Monitoring Data Analytics","authors":"Saptarshi Das, Ahmed Alzimami","doi":"10.1109/ICAISC56366.2023.10085504","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085504","url":null,"abstract":"This paper uses the Bayesian optimization for fitting Ensemble regression models for tuning the machine learning model hyperparameters with reduced computation. We use the Pune Smart City air quality monitoring dataset with temporal variation of hazardous chemical pollutants in the air. The aim here is to reliably predict the suspended particulates as the air quality metrics using other environmental variables, considering linear models and nonlinear ensemble of tree models. To achieve good predictive accuracy a computationally expensive optimization method is required which has been achieved using the Gaussian Process surrogate assisted Bayesian optimization. We also show the diagnostics plots of the residuals from the nonlinear models to explain model quality.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126592579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085379
Rakesh Kumar, B. Sharma, S. Shekhar, I. Dhaou, S. Singhal
Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals’ wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city.
{"title":"Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning","authors":"Rakesh Kumar, B. Sharma, S. Shekhar, I. Dhaou, S. Singhal","doi":"10.1109/ICAISC56366.2023.10085379","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085379","url":null,"abstract":"Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals’ wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10084976
Shahad Mohammed Alenazy, Raghad Mohammed Alenazy, Mohammed Ishaque
By reviewing a few essential factors that should be considered, we may discuss the significance of securing an organization’s important commercial information assets. On the basis of this, it is important Information security should start off as a corporate governance need, be incorporated into the corporate decision-making process, and be a priority for top management, including the board of directors and CEO. As a result, this article underlines the significance of developing an information security governance (ISG) framework and integrating information security into corporate governance. In addition, I’ll write the research based on the training experiment that I attended in Jeddah Municipality and what learned by dealing with some security issues there and will offer a framework to aid an organization’s ISG efforts.
{"title":"Governance of Information Security and Its Role In Reducing the Risk of Electronic Accounting Information System","authors":"Shahad Mohammed Alenazy, Raghad Mohammed Alenazy, Mohammed Ishaque","doi":"10.1109/ICAISC56366.2023.10084976","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10084976","url":null,"abstract":"By reviewing a few essential factors that should be considered, we may discuss the significance of securing an organization’s important commercial information assets. On the basis of this, it is important Information security should start off as a corporate governance need, be incorporated into the corporate decision-making process, and be a priority for top management, including the board of directors and CEO. As a result, this article underlines the significance of developing an information security governance (ISG) framework and integrating information security into corporate governance. In addition, I’ll write the research based on the training experiment that I attended in Jeddah Municipality and what learned by dealing with some security issues there and will offer a framework to aid an organization’s ISG efforts.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124368866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085007
A. Alawadhi, Abdullah Almogahed, E. Azrag
The Internet of Everything (IoE)-based smart services are becoming more prominent as a result of the growing demands placed on wireless networks. Despite 5G having the ability to serve a wide range of IoE-based applications, they are unable to fully satisfy the needs of the newest intelligent systems. Therefore, 6G is the next generation for the IoT, IoE, and cellular networks, which aim to significantly improve smart services quality, such as maximum throughput and reduced latency. The number of internet-connected smart devices grows exponentially on a daily basis, resulting in Big Data. Several other cloud-based implementations use data centers as centralized servers for handling data collected by edge devices. This model places ever-increasing demands on computational infrastructure and communication, with unavoidable consequences for Experience and Quality-of-Service. By attempting to obtain cloud abilities closer to end users, edge computing is a novel technology which enables the development for 6G by attempting to overcome typical cloud weaknesses such as high latency and a lack of security. The purpose of this paper is to discuss challenges and opportunities in edge computing technology in 6G IoE.
{"title":"Towards Edge Computing for 6G Internet of Everything: Challenges and Opportunities","authors":"A. Alawadhi, Abdullah Almogahed, E. Azrag","doi":"10.1109/ICAISC56366.2023.10085007","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085007","url":null,"abstract":"The Internet of Everything (IoE)-based smart services are becoming more prominent as a result of the growing demands placed on wireless networks. Despite 5G having the ability to serve a wide range of IoE-based applications, they are unable to fully satisfy the needs of the newest intelligent systems. Therefore, 6G is the next generation for the IoT, IoE, and cellular networks, which aim to significantly improve smart services quality, such as maximum throughput and reduced latency. The number of internet-connected smart devices grows exponentially on a daily basis, resulting in Big Data. Several other cloud-based implementations use data centers as centralized servers for handling data collected by edge devices. This model places ever-increasing demands on computational infrastructure and communication, with unavoidable consequences for Experience and Quality-of-Service. By attempting to obtain cloud abilities closer to end users, edge computing is a novel technology which enables the development for 6G by attempting to overcome typical cloud weaknesses such as high latency and a lack of security. The purpose of this paper is to discuss challenges and opportunities in edge computing technology in 6G IoE.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122996830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085461
M. Kamal, Ali Asghar, I. Ashraf
The world is dedicated to increasing renewable energy, achieving energy savings, and reducing greenhouse gas (GHG) production to help global efforts combat climate change. India has committed to providing access to power in rural regions using accessible renewable energy sources to reduce greenhouse gas emissions. The microgrid powered by renewable resources can meet local energy needs and add the remaining excess power to the electric grid. Additionally, it can minimize the issue of greenhouse emissions. This study examined the best grid-connected microgrid configuration and simulation for a residence in Lucknow, Uttar Pradesh, India. Using the HOMER energy application, a model is created and tested. A battery bank serves as the microgrid’s storage system, and wind turbine, photovoltaic, and diesel generators serve as its generating resources. The simulation results show that, in terms of meeting the demand for the entire load, the proposed home microgrid is more affordable than the power grid. The overall net present cost of the ideal system is computed to be ${$}$ 17,443 for the region under consideration at an energy cost of 010/kWh. Additionally, the proposed grid’s environmental effect is examined, and it is discovered that the architecture of the proposed grid emits just a small amount of dangerous greenhouse gases.
{"title":"Modelling and Evaluation of Renewable Integrated Grid-Connected Microgrid for Cost-Effective Energy Management","authors":"M. Kamal, Ali Asghar, I. Ashraf","doi":"10.1109/ICAISC56366.2023.10085461","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085461","url":null,"abstract":"The world is dedicated to increasing renewable energy, achieving energy savings, and reducing greenhouse gas (GHG) production to help global efforts combat climate change. India has committed to providing access to power in rural regions using accessible renewable energy sources to reduce greenhouse gas emissions. The microgrid powered by renewable resources can meet local energy needs and add the remaining excess power to the electric grid. Additionally, it can minimize the issue of greenhouse emissions. This study examined the best grid-connected microgrid configuration and simulation for a residence in Lucknow, Uttar Pradesh, India. Using the HOMER energy application, a model is created and tested. A battery bank serves as the microgrid’s storage system, and wind turbine, photovoltaic, and diesel generators serve as its generating resources. The simulation results show that, in terms of meeting the demand for the entire load, the proposed home microgrid is more affordable than the power grid. The overall net present cost of the ideal system is computed to be ${$}$ 17,443 for the region under consideration at an energy cost of 010/kWh. Additionally, the proposed grid’s environmental effect is examined, and it is discovered that the architecture of the proposed grid emits just a small amount of dangerous greenhouse gases.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129070392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085104
Bibhuprasad Sahu, J. Ravindra, S. Mohanty, Amrutanshu Panigrahi
In the era of machine learning, microarray data play a crucial role in identifying cancer diseases. The impact of redundant and noisy features degrades the learning model’s performance. It may also increase the computational cost. The curse of dimensionality is the major concern in the case of microarray datasets. To eliminate this issue, feature selection methods play an effective role. This study proposes a hybrid filter-wrapper feature selection model using mRMR_Plus as a filter and grasshopper optimization algorithm as a wrapper. In the first stage of the proposed model, a ranked base filter mRMR_Plus is used to identify the top-ranked features from the original dataset. Cross-operator embedded simulated annealing (SA) is adopted to basic grasshopper optimization to develop a new wrapper model. The proposed model was tested with different cancer datasets to recognize the best optimal features. The result of mRMR-Plus-GO-SA is compared with different existing approaches. From the result and the comparative study, it’s noteworthy to state that the new mRMR_Plus-GO-SA filter wrapper model performs far better as compared to its counterparts in terms of the number of features selected and accuracy.
{"title":"Hybrid grasshopper optimization algorithm with simulated annealing for feature selection using high dimensional dataset","authors":"Bibhuprasad Sahu, J. Ravindra, S. Mohanty, Amrutanshu Panigrahi","doi":"10.1109/ICAISC56366.2023.10085104","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085104","url":null,"abstract":"In the era of machine learning, microarray data play a crucial role in identifying cancer diseases. The impact of redundant and noisy features degrades the learning model’s performance. It may also increase the computational cost. The curse of dimensionality is the major concern in the case of microarray datasets. To eliminate this issue, feature selection methods play an effective role. This study proposes a hybrid filter-wrapper feature selection model using mRMR_Plus as a filter and grasshopper optimization algorithm as a wrapper. In the first stage of the proposed model, a ranked base filter mRMR_Plus is used to identify the top-ranked features from the original dataset. Cross-operator embedded simulated annealing (SA) is adopted to basic grasshopper optimization to develop a new wrapper model. The proposed model was tested with different cancer datasets to recognize the best optimal features. The result of mRMR-Plus-GO-SA is compared with different existing approaches. From the result and the comparative study, it’s noteworthy to state that the new mRMR_Plus-GO-SA filter wrapper model performs far better as compared to its counterparts in terms of the number of features selected and accuracy.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129087392","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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