Pub Date : 2023-01-23DOI: 10.1109/ICAISC56366.2023.10085561
Hassanin M. Al-Barhamtoshy, Hanen Himdi, Mohamad Alyahya
With Countless Arabic news articles published daily; users have become increasingly concerned about obtaining news from credible sources. Nonetheless, to individuals, credible news sources are associated with certain countries where users have faith. Therefore, detecting the source of a news article is imperative to fake news detection and enables users a better trust in their consuming news. This paper introduces to create, filter, analyze, and evaluate a domain services-specific Arabic dataset for pilgrims. The Arabic Pilgrim Services (ArPiS) dataset is a collection of approximately 30,000 news, collected across three different Arabic countries and regions. The paper presents a creation for pilgrims’ opinions measurement services dataset for text mining, text classification, clustering, and text summarization. The default basic search methods start with 124 web sites of Arabic news. Then, many of filtering features have been done to limit the dataset by pilgrim subjected services. A lot of topics are addressed, and a lot of filter with a discussion group have been made with many opinions| and extra comments. The huge of the collected data need some kind of additional effort and more analysis to produce valuable dataset. Balanced dataset is one of this extra effort, we are going to create. Therefore, the collected and annotated dataset represents real news for pilgrims’ services. So, we need to build additional quantity of these data to be fake news. Accordingly, a precondition procedure invoked as a methodology to create and then annotate such dataset.
{"title":"Arabic Pilgrim Services Dataset: Creating and Analysis","authors":"Hassanin M. Al-Barhamtoshy, Hanen Himdi, Mohamad Alyahya","doi":"10.1109/ICAISC56366.2023.10085561","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085561","url":null,"abstract":"With Countless Arabic news articles published daily; users have become increasingly concerned about obtaining news from credible sources. Nonetheless, to individuals, credible news sources are associated with certain countries where users have faith. Therefore, detecting the source of a news article is imperative to fake news detection and enables users a better trust in their consuming news. This paper introduces to create, filter, analyze, and evaluate a domain services-specific Arabic dataset for pilgrims. The Arabic Pilgrim Services (ArPiS) dataset is a collection of approximately 30,000 news, collected across three different Arabic countries and regions. The paper presents a creation for pilgrims’ opinions measurement services dataset for text mining, text classification, clustering, and text summarization. The default basic search methods start with 124 web sites of Arabic news. Then, many of filtering features have been done to limit the dataset by pilgrim subjected services. A lot of topics are addressed, and a lot of filter with a discussion group have been made with many opinions| and extra comments. The huge of the collected data need some kind of additional effort and more analysis to produce valuable dataset. Balanced dataset is one of this extra effort, we are going to create. Therefore, the collected and annotated dataset represents real news for pilgrims’ services. So, we need to build additional quantity of these data to be fake news. Accordingly, a precondition procedure invoked as a methodology to create and then annotate such dataset.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"75 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":"130017736","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/ICAISC56366.2023.10085507
Abhimanyu Bhowmik, Madhushree Sannigrahi, P. Dutta, Saubhik Bandyopadhyay
The semantic sensor node interprets sensor data from the physical devices that make observations using Semantic Web technology and reasoning. One example is the use of conceptual frameworks in automated gardening systems by collecting plant health characteristics and pest resolution models and optimum temperature control models on a regular basis and passing it to a gardener or a caretaker in a flat making it feasible to monitor plant health status from remote locations. At regular intervals, the Bolt IoT platform collects data on the availability of sunlight and soil moisture content for the plants. After processing and validating data with Integromat (cloud-based logic design), an SMS is delivered to our smartphone via Twilio (cloud communication platform), and the user performs the necessary actions depending on the data. This smart horticulture system will give the user ease and comfort even when they are not physically there, allowing people to better care for our garden.
{"title":"Using Edge Computing framework with the Internet of Things for Intelligent Vertical Gardening","authors":"Abhimanyu Bhowmik, Madhushree Sannigrahi, P. Dutta, Saubhik Bandyopadhyay","doi":"10.1109/ICAISC56366.2023.10085507","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085507","url":null,"abstract":"The semantic sensor node interprets sensor data from the physical devices that make observations using Semantic Web technology and reasoning. One example is the use of conceptual frameworks in automated gardening systems by collecting plant health characteristics and pest resolution models and optimum temperature control models on a regular basis and passing it to a gardener or a caretaker in a flat making it feasible to monitor plant health status from remote locations. At regular intervals, the Bolt IoT platform collects data on the availability of sunlight and soil moisture content for the plants. After processing and validating data with Integromat (cloud-based logic design), an SMS is delivered to our smartphone via Twilio (cloud communication platform), and the user performs the necessary actions depending on the data. This smart horticulture system will give the user ease and comfort even when they are not physically there, allowing people to better care for our garden.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"27 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":"131707727","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/ICAISC56366.2023.10085107
Azhar Imran, M. Fahim, Abdulkareem Alzahrani, Safa Fahim, K. Alheeti, S. Rehman
In recent years, Sentimental Analysis has enticed many researchers in this field. Due to the lack of suitable datasets, many scientists and researchers faced hindrances in their research. We used Semeval dataset because it’s the authentic dataset for computational sentimental Analysis. When it comes to natural catastrophes, political turmoil, and terrorism, social scientists and psychologists are interested in learning how individuals express their feelings and opinions. In this paper, we present the approach to the SemEva1-2017 dataset. As we know, with the advancement of technology, social media have established strong worldwide connectivity and information sharing. The wide use of social media and media-networking sites produced an unprecedented amount of data. Sharing information using these websites has become very common. To detect the triggering factors has become necessary to understand the behavioural and emotional state to avoid anti-social behaviour and extreme or impulsive responses. We reveal to identify the emotional textual data using different strategies. Classification of the Tweets according to the Sentimental Analysis has an important role in the social, economic, and political world s. The effective strategy for tackling and coping with it is to use computational techniques to identify the speech type. For feature extraction, we use a variety of machine learning classifiers. It’s crucial to detect related features in a text correctly. As a result, using and improving NLP approaches can aid in improved understanding and analysis of data. Briefly, we use an unsupervised. TF-IDF for the Feature Extraction to train the word Embedding Techniques that are tuned into transform training data and transformed Test data. The model is finally initialized using vectorization on Twitter sentiment analysis to train the latter. Then, transformed the model to create the transformed dataset. The major findings and outcomes of SemEva1-2017 on Identifying and Categorizing the Sentiments of the Language in social media of Twitter are presented and evaluated results based on applying different classifiers for Machine learning Modeling.
{"title":"Twitter Sentimental Analysis using Machine Learning Approaches for SemeVal Dataset","authors":"Azhar Imran, M. Fahim, Abdulkareem Alzahrani, Safa Fahim, K. Alheeti, S. Rehman","doi":"10.1109/ICAISC56366.2023.10085107","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085107","url":null,"abstract":"In recent years, Sentimental Analysis has enticed many researchers in this field. Due to the lack of suitable datasets, many scientists and researchers faced hindrances in their research. We used Semeval dataset because it’s the authentic dataset for computational sentimental Analysis. When it comes to natural catastrophes, political turmoil, and terrorism, social scientists and psychologists are interested in learning how individuals express their feelings and opinions. In this paper, we present the approach to the SemEva1-2017 dataset. As we know, with the advancement of technology, social media have established strong worldwide connectivity and information sharing. The wide use of social media and media-networking sites produced an unprecedented amount of data. Sharing information using these websites has become very common. To detect the triggering factors has become necessary to understand the behavioural and emotional state to avoid anti-social behaviour and extreme or impulsive responses. We reveal to identify the emotional textual data using different strategies. Classification of the Tweets according to the Sentimental Analysis has an important role in the social, economic, and political world s. The effective strategy for tackling and coping with it is to use computational techniques to identify the speech type. For feature extraction, we use a variety of machine learning classifiers. It’s crucial to detect related features in a text correctly. As a result, using and improving NLP approaches can aid in improved understanding and analysis of data. Briefly, we use an unsupervised. TF-IDF for the Feature Extraction to train the word Embedding Techniques that are tuned into transform training data and transformed Test data. The model is finally initialized using vectorization on Twitter sentiment analysis to train the latter. Then, transformed the model to create the transformed dataset. The major findings and outcomes of SemEva1-2017 on Identifying and Categorizing the Sentiments of the Language in social media of Twitter are presented and evaluated results based on applying different classifiers for Machine learning Modeling.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"14 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114038131","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/ICAISC56366.2023.10085382
Sunday Achimugu, Sunday Achimugu, Lukman Adewale Ajao, Usman Abraham Usman
Antenna implementations play a significant role in the field of wireless communication and aid the transmission and reception of electromagnetic wave propagation. The shape, size, and parameters of this crucial system depend on the certain range of frequency band allocation. The WiFi network operates at the spectrum of 2.4GHz to 5.9GHz, and the recent 802.11 standard ranges up to 60GHz. But the coverage area for indoor or outdoor deployment is very narrow which requires efficient antenna coverage from far distance to the access point network. This work proposed an optimized directional half-wave dipole antenna, adaptable for affixation to panel and yagi-uda antennas. This technique of antenna coverage optimization was achieved through the varying of antenna parameters to obtain better performance with reference to 100W rated, 2.4GHz, and 2.18dB gain WiFi half-wave dipole having a range of 150m. The optimized antenna operates at a frequency of 2.4GHz – 5.9GHz with a beam distance of 500m and an improved gain of 4.73dB. This obtained result shows a better performance in comparison to the understudied antenna, which makes it a candidate for WiFi and low-frequency broadband dipole antenna applications.
{"title":"An Optimized Half Wave Dipole Antenna for the Transmission of WiFi and Broadband Networks","authors":"Sunday Achimugu, Sunday Achimugu, Lukman Adewale Ajao, Usman Abraham Usman","doi":"10.1109/ICAISC56366.2023.10085382","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085382","url":null,"abstract":"Antenna implementations play a significant role in the field of wireless communication and aid the transmission and reception of electromagnetic wave propagation. The shape, size, and parameters of this crucial system depend on the certain range of frequency band allocation. The WiFi network operates at the spectrum of 2.4GHz to 5.9GHz, and the recent 802.11 standard ranges up to 60GHz. But the coverage area for indoor or outdoor deployment is very narrow which requires efficient antenna coverage from far distance to the access point network. This work proposed an optimized directional half-wave dipole antenna, adaptable for affixation to panel and yagi-uda antennas. This technique of antenna coverage optimization was achieved through the varying of antenna parameters to obtain better performance with reference to 100W rated, 2.4GHz, and 2.18dB gain WiFi half-wave dipole having a range of 150m. The optimized antenna operates at a frequency of 2.4GHz – 5.9GHz with a beam distance of 500m and an improved gain of 4.73dB. This obtained result shows a better performance in comparison to the understudied antenna, which makes it a candidate for WiFi and low-frequency broadband dipole antenna applications.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"30 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":"125077578","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}
In the revolutionary era of research, there has been quite rapid improvement in Research fields with ample amount of publishing of research papers, now-a-days. The developed model focuses on two main approaches, “Levenshtein’s Distance Algorithm” and “Google Query Search” method to combinedly innovate a new aspect for tackling plagiarism issues. To assist precise and authenticate referring links similar to the suspicious plagiarized text in one’s review or research paper. The developed model has achieved a prominent level of accuracy percentage mentioning a few minor assets like, preventing the redirection of referring links to the junk sites.
{"title":"Plagiarism Checker & Link Advisor using concepts of Levenshtein Distance Algorithm with Google Query Search - An Approach","authors":"Dhanraj Arvind Nandurkar, Priyanka Ujjainkar, Bhakti Miglani, Ayush Kanojiya","doi":"10.1109/ICAISC56366.2023.10085404","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085404","url":null,"abstract":"In the revolutionary era of research, there has been quite rapid improvement in Research fields with ample amount of publishing of research papers, now-a-days. The developed model focuses on two main approaches, “Levenshtein’s Distance Algorithm” and “Google Query Search” method to combinedly innovate a new aspect for tackling plagiarism issues. To assist precise and authenticate referring links similar to the suspicious plagiarized text in one’s review or research paper. The developed model has achieved a prominent level of accuracy percentage mentioning a few minor assets like, preventing the redirection of referring links to the junk sites.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"291 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":"131470169","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/ICAISC56366.2023.10085629
V. Bijlani
On an average, 60% to 80% of the global energy consumption is attributed to cities, which generate as much as 70% of the human-induced greenhouse gas (GHG) emissions [1]. Buildings consume 38% [2] of the total GHGs, the single highest source of energy consumption worldwide. They also use up a large slice of natural resources, justifying the urgency to recast them into more sustainable, energy-efficient, spaces. In a world that is being transformed by tech, we need more efficient infrastructure and more sustainable living spaces. Cities built on Smart technology, fed with large amounts of data, and harnessing ‘glocal’ solutions to support sustainability can create a resilient environment to live in. Supported by a framework that is both financially viable and operationally practical, such a model can secure health, resilience, and a sustainable way of life for future generations. This paper examines the current trends and the way forward for sustainable Smart buildings to create cities that are intelligent, connected, safe, affordable, and green.
{"title":"Smart Buildings for Sustainable Smart Cities","authors":"V. Bijlani","doi":"10.1109/ICAISC56366.2023.10085629","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085629","url":null,"abstract":"On an average, 60% to 80% of the global energy consumption is attributed to cities, which generate as much as 70% of the human-induced greenhouse gas (GHG) emissions [1]. Buildings consume 38% [2] of the total GHGs, the single highest source of energy consumption worldwide. They also use up a large slice of natural resources, justifying the urgency to recast them into more sustainable, energy-efficient, spaces. In a world that is being transformed by tech, we need more efficient infrastructure and more sustainable living spaces. Cities built on Smart technology, fed with large amounts of data, and harnessing ‘glocal’ solutions to support sustainability can create a resilient environment to live in. Supported by a framework that is both financially viable and operationally practical, such a model can secure health, resilience, and a sustainable way of life for future generations. This paper examines the current trends and the way forward for sustainable Smart buildings to create cities that are intelligent, connected, safe, affordable, and green.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"136 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":"133638097","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/ICAISC56366.2023.10085464
S. Mahfooz, Ahmed Alhasani, A. Hassan
Smart cities can immensely benefit from the applications of Artificial Intelligence. These cities are highly attractive by their rich pull factors like the provision of facilities for safe and sustainable living. Sustainable Development Goals (SDGs) by the United Nations are the blueprint to improve the standards of sustainable living in all countries. The impact and achievement of SDGs are regularly assessed at country-level. To briefly describe a part of this process, we consider the current status of GCC countries regarding their achievements for SDG11.6.2 indicator that focuses on air quality. World Health organization regularly updates air quality database and when a source of reliable air quality data is missing, air quality in cities is modelled. We use LSTM neural network that learns from historical values of air quality data and predicts new values. This alternative approach may be used to confirm missing or inconsistent PM2.5 values. The objectives of our studies are to highlight one of the possible modern applications of AI to predict missing or unreported data and to leverage the concept of SDGs driven smart cities. We evaluate the performance of the LSTM model, and our results show that this model is capable of predicting data with acceptable accuracy.
{"title":"SDG-11.6.2 Indicator and Predictions of PM2.5 using LSTM Neural Network","authors":"S. Mahfooz, Ahmed Alhasani, A. Hassan","doi":"10.1109/ICAISC56366.2023.10085464","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085464","url":null,"abstract":"Smart cities can immensely benefit from the applications of Artificial Intelligence. These cities are highly attractive by their rich pull factors like the provision of facilities for safe and sustainable living. Sustainable Development Goals (SDGs) by the United Nations are the blueprint to improve the standards of sustainable living in all countries. The impact and achievement of SDGs are regularly assessed at country-level. To briefly describe a part of this process, we consider the current status of GCC countries regarding their achievements for SDG11.6.2 indicator that focuses on air quality. World Health organization regularly updates air quality database and when a source of reliable air quality data is missing, air quality in cities is modelled. We use LSTM neural network that learns from historical values of air quality data and predicts new values. This alternative approach may be used to confirm missing or inconsistent PM2.5 values. The objectives of our studies are to highlight one of the possible modern applications of AI to predict missing or unreported data and to leverage the concept of SDGs driven smart cities. We evaluate the performance of the LSTM model, and our results show that this model is capable of predicting data with acceptable accuracy.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"37 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":"132200413","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/ICAISC56366.2023.10085212
Mostafa A. Elhosseini, Natheer Khlaif Gharaibeh, W. Abu-Ain
Consider the most important lessons learned from the global achievements and disappointments of the previous year. It was a year filled with pandemics that exacerbated massive geopolitical, social, and economic shocks on a worldwide scale, bringing out the worst and best in people. However, the past two years have demonstrated the fragility of global institutions in numerous industries, including medicine, hospitality, travel, and commerce. It also reflects the resilience of the international system with the introduction of various vaccinations and concentrated worldwide efforts against pandemic threats. Conventional and cutting-edge technology approaches are needed to attack COVID-19 and put the situation under control. This paper’s primary purpose is to systematically study trends in technology solutions for smart healthcare systems – for example, artificial intelligence (AI) and big data (BD) analytics, which will help save the world. These AI solutions facilitate innovative administrations, adaptability, productivity, and efficiency by developing related frameworks. Specifically, this study identifies AI and Big Data contributions that should be incorporated into smart healthcare systems. It also studies the application of big data analytics and AI to offer users insights and help them to plan and presents models for intelligent healthcare systems based on AI and big data analytics.
{"title":"Trends in Smart Healthcare Systems for Smart Cities Applications","authors":"Mostafa A. Elhosseini, Natheer Khlaif Gharaibeh, W. Abu-Ain","doi":"10.1109/ICAISC56366.2023.10085212","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085212","url":null,"abstract":"Consider the most important lessons learned from the global achievements and disappointments of the previous year. It was a year filled with pandemics that exacerbated massive geopolitical, social, and economic shocks on a worldwide scale, bringing out the worst and best in people. However, the past two years have demonstrated the fragility of global institutions in numerous industries, including medicine, hospitality, travel, and commerce. It also reflects the resilience of the international system with the introduction of various vaccinations and concentrated worldwide efforts against pandemic threats. Conventional and cutting-edge technology approaches are needed to attack COVID-19 and put the situation under control. This paper’s primary purpose is to systematically study trends in technology solutions for smart healthcare systems – for example, artificial intelligence (AI) and big data (BD) analytics, which will help save the world. These AI solutions facilitate innovative administrations, adaptability, productivity, and efficiency by developing related frameworks. Specifically, this study identifies AI and Big Data contributions that should be incorporated into smart healthcare systems. It also studies the application of big data analytics and AI to offer users insights and help them to plan and presents models for intelligent healthcare systems based on AI and big data analytics.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"93 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":"134210694","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/ICAISC56366.2023.10085083
Mohammed Abdullah Danah, F. Bourennani, Abdullah Saad Musaed Al-Shahrani
King Abdul Aziz Airport in Jeddah receives over 3 million visitors who come to complete the pilgrimage in Mecca. The airport administration is challenged by the high number of passengers and must setup an optimum management airport system to provide a high level of services during their transition at the airport and to reduce the waiting time. In this work, we propose the use of genetic algorithms to build an intelligent airport management system for an optimal passenger transition time in order to improve the logistics during the Hajj seasons.The efficiency of the proposed system is demonstrated through a real case-study using real data from an airport, we were able to apply an NGSAII algorithm that proved to optimize up to 29% of time in some cases.
{"title":"Intelligent airport management system","authors":"Mohammed Abdullah Danah, F. Bourennani, Abdullah Saad Musaed Al-Shahrani","doi":"10.1109/ICAISC56366.2023.10085083","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085083","url":null,"abstract":"King Abdul Aziz Airport in Jeddah receives over 3 million visitors who come to complete the pilgrimage in Mecca. The airport administration is challenged by the high number of passengers and must setup an optimum management airport system to provide a high level of services during their transition at the airport and to reduce the waiting time. In this work, we propose the use of genetic algorithms to build an intelligent airport management system for an optimal passenger transition time in order to improve the logistics during the Hajj seasons.The efficiency of the proposed system is demonstrated through a real case-study using real data from an airport, we were able to apply an NGSAII algorithm that proved to optimize up to 29% of time in some cases.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"44 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":"115514710","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/ICAISC56366.2023.10085096
Masahiro Taima, T. Daimon
For an appropriate communication design between an automated vehicle and road users, we need to understand unsafe and inefficient communications in public road environments. This study investigated unsafe and inefficient communications between automated buses and road users and analyzed the underlying factors. We collected video data from a camera attached to a bus for 233 days in a field operation test (FOT) at seven locations in Japan and observed 22,199 communications between the automated bus and road users. Consequently, we observed several types of unsafe and inefficient communications. We found that specific automated bus characteristics, such as absence of driver’s action, unfamiliar appearance, and fixed trajectory, caused these unsafe and inefficient communications in crossing and overtaking scenarios. Our study indicates the necessity of some improvements for implicit/explicit cues from an automated bus, along with the education of residents and visitors.
{"title":"Unsafe and inefficient communication between automated buses and road users on public roads in Japan","authors":"Masahiro Taima, T. Daimon","doi":"10.1109/ICAISC56366.2023.10085096","DOIUrl":"https://doi.org/10.1109/ICAISC56366.2023.10085096","url":null,"abstract":"For an appropriate communication design between an automated vehicle and road users, we need to understand unsafe and inefficient communications in public road environments. This study investigated unsafe and inefficient communications between automated buses and road users and analyzed the underlying factors. We collected video data from a camera attached to a bus for 233 days in a field operation test (FOT) at seven locations in Japan and observed 22,199 communications between the automated bus and road users. Consequently, we observed several types of unsafe and inefficient communications. We found that specific automated bus characteristics, such as absence of driver’s action, unfamiliar appearance, and fixed trajectory, caused these unsafe and inefficient communications in crossing and overtaking scenarios. Our study indicates the necessity of some improvements for implicit/explicit cues from an automated bus, along with the education of residents and visitors.","PeriodicalId":422888,"journal":{"name":"2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC)","volume":"53 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":"121423389","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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