Pub Date : 2022-02-08DOI: 10.14201/adcaij202110401418
Roopesh Ranjan, A. Daniel
The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.
{"title":"Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques","authors":"Roopesh Ranjan, A. Daniel","doi":"10.14201/adcaij202110401418","DOIUrl":"https://doi.org/10.14201/adcaij202110401418","url":null,"abstract":"The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"5 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76386760","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 : 2022-02-08DOI: 10.14201/adcaij2021104381399
Zahida Rahman, Altaf Hussain, Hussain Shah, M. Arshad
Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.
聚类是一种无监督的机器学习过程，它将数据对象分组到集群中，使同一集群中的对象彼此高度相似。互联网上乌尔都语文本的数量每天都在高速增长。手工对乌尔都语新闻进行分组几乎是不可能的，迫切需要建立一种基于乌尔都语新闻文档相似度的聚类机制。乌尔都语新闻文档的准确聚类是一个研究课题，可以采用相似度技术，即Jaccard and Dice系数和聚类k-mean算法来解决这一问题。本研究采用Jaccard and Dice系数在python编程语言中寻找乌尔都语新闻文档的相似度得分。为了聚类，将相似度结果加载到Waikato Environment For Knowledge Analysis (WEKA)中，利用k-mean算法将乌尔都语新闻文档聚类为5类。所获得的聚类结果根据准确性和均方误差(MSE)进行评估。Jaccard的准确率和MSE分别为85%和44.4%，Dice的准确率和MSE分别为87%和35.76%。实验结果表明，在准确率和均方差的基础上，Dice系数优于Jaccard相似度。
{"title":"Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity","authors":"Zahida Rahman, Altaf Hussain, Hussain Shah, M. Arshad","doi":"10.14201/adcaij2021104381399","DOIUrl":"https://doi.org/10.14201/adcaij2021104381399","url":null,"abstract":"Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"39 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80410171","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 : 2022-02-08DOI: 10.14201/adcaij2021104361379
Azhee Wria Muhamada, A. Mohammed
The subject of human activity recognition is considered an important goal in the domain of computer vision from the beginning of its development and has reached new levels. It is also thought of as a simple procedure. Problems arise in fast-moving and advanced scenes, and the numerical analysis of artificial intelligence (AI) through activity prediction mistreatment increased the attention of researchers to study. Having decent methodological and content related variations, several datasets were created to address the evaluation of these ways. Human activities play an important role but with challenging characteristic in various fields. Many applications exist in this field, such as smart home, helpful AI, HCI (Human-Computer Interaction), advancements in protection in applications such as transportation, education, security, and medication management, including falling or helping elderly in medical drug consumption. The positive impact of deep learning techniques on many vision applications leads to deploying these ways in video processing. Analysis of human behavior activities involves major challenges when human presence is concerned. One individual can be represented in multiple video sequences through skeleton, motion and/or abstract characteristics. This work aims to address human presence by combining many options and utilizing a new RNN structure for activities. The paper focuses on recent advances in machine learning-assisted action recognition. Existing modern techniques for the recognition of actions and prediction similarly because the future scope for the analysis is mentioned accuracy within the review paper.
{"title":"Review on recent Computer Vision Methods for Human Action Recognition","authors":"Azhee Wria Muhamada, A. Mohammed","doi":"10.14201/adcaij2021104361379","DOIUrl":"https://doi.org/10.14201/adcaij2021104361379","url":null,"abstract":"\u0000 \u0000 \u0000The subject of human activity recognition is considered an important goal in the domain of computer vision from the beginning of its development and has reached new levels. It is also thought of as a simple procedure. Problems arise in fast-moving and advanced scenes, and the numerical analysis of artificial intelligence (AI) through activity prediction mistreatment increased the attention of researchers to study. Having decent methodological and content related variations, several datasets were created to address the evaluation of these ways. Human activities play an important role but with challenging characteristic in various fields. Many applications exist in this field, such as smart home, helpful AI, HCI (Human-Computer Interaction), advancements in protection in applications such as transportation, education, security, and medication management, including falling or helping elderly in medical drug consumption. The positive impact of deep learning techniques on many vision applications leads to deploying these ways in video processing. Analysis of human behavior activities involves major challenges when human presence is concerned. One individual can be represented in multiple video sequences through skeleton, motion and/or abstract characteristics. This work aims to address human presence by combining many options and utilizing a new RNN structure for activities. The paper focuses on recent advances in machine learning-assisted action recognition. \u0000Existing modern techniques for the recognition of actions and prediction similarly because the future scope for the analysis is mentioned accuracy within the review paper. \u0000 \u0000 \u0000","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"7 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89748915","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 : 2022-02-08DOI: 10.14201/adcaij2021104321337
Altaf Hussain, Habib Ullah Khan, S. Nazir, Ijaz Ullah, T. Hussain
Flying Ad-hoc Network (FANET) is a special member/class of Mobile Ad-hoc Network (MANET) in which the movable nodes are known as by the name of Unmanned Aerial Vehicles (UAVs) that are operated from a long remote distance in which there is no human personnel involved. It is an ad-hoc network in which the UAVs can more in 3D ways simultaneously in the air without any onboard pilot. In other words, this is a pilot free ad-hoc network also known as Unmanned Aerial System (UAS) and the component introduced for such a system is known as UAV. There are many single UAV applications but using multiple UAVs system cooperating can be helpful in many ways in the field of wireless communication. Deployments of these small UAVs are quick and flexible which overcome the limitation of traditional ad hoc networks. FANETs differ from other kinds of ad hoc networks and envisioned to play an important role where infrastructure operations are not available and assigned tasks are too dull, dirty, or dangerous for humans. Moreover, setting up to bolster the range and performance of small UAV in ad hoc network lead to emergent evolution with its high stability, quick deployment, and ease-of-use for the formation of the network. Routing and task allocation are the challenging research areas of the network with ad hoc nodes. The paper overview based on the study of biological inspired routing protocols (Moth-and-Ant and Bee Ad-Hoc) routing protocols.
{"title":"Taking FANET to Next Level","authors":"Altaf Hussain, Habib Ullah Khan, S. Nazir, Ijaz Ullah, T. Hussain","doi":"10.14201/adcaij2021104321337","DOIUrl":"https://doi.org/10.14201/adcaij2021104321337","url":null,"abstract":"Flying Ad-hoc Network (FANET) is a special member/class of Mobile Ad-hoc Network (MANET) in which the movable nodes are known as by the name of Unmanned Aerial Vehicles (UAVs) that are operated from a long remote distance in which there is no human personnel involved. It is an ad-hoc network in which the UAVs can more in 3D ways simultaneously in the air without any onboard pilot. In other words, this is a pilot free ad-hoc network also known as Unmanned Aerial System (UAS) and the component introduced for such a system is known as UAV. There are many single UAV applications but using multiple UAVs system cooperating can be helpful in many ways in the field of wireless communication. Deployments of these small UAVs are quick and flexible which overcome the limitation of traditional ad hoc networks. FANETs differ from other kinds of ad hoc networks and envisioned to play an important role where infrastructure operations are not available and assigned tasks are too dull, dirty, or dangerous for humans. Moreover, setting up to bolster the range and performance of small UAV in ad hoc network lead to emergent evolution with its high stability, quick deployment, and ease-of-use for the formation of the network. Routing and task allocation are the challenging research areas of the network with ad hoc nodes. The paper overview based on the study of biological inspired routing protocols (Moth-and-Ant and Bee Ad-Hoc) routing protocols.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"44 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76839582","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 : 2022-01-01DOI: 10.1007/978-3-030-94662-3
{"title":"Distributed Artificial Intelligence: Third International Conference, DAI 2021, Shanghai, China, December 17–18, 2021, Proceedings","authors":"","doi":"10.1007/978-3-030-94662-3","DOIUrl":"https://doi.org/10.1007/978-3-030-94662-3","url":null,"abstract":"","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"28 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74181622","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}
AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research.
{"title":"Learning in AI Processor","authors":"Xinhua Wang, Weikang Wu","doi":"10.30564/aia.v3i2.3878","DOIUrl":"https://doi.org/10.30564/aia.v3i2.3878","url":null,"abstract":"AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"88 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81127325","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}
Febronie Nambajemariya, Yongshun Wang, Twizerane Jean D’Amour, Kwizera Niyigena Vincent DePaul, Yao Hu
With the deployment of Connected and Automated Vehicles in the coming decades, road transportation will experience a significant upheaval. CAVs (Connected and Autonomous Vehicles) have been a main emphasis of Transportation and the automotive sector, and the future of transportation system analysis is widely anticipated. The examination and future development of CAVs technology has been the subject of numerous researches. However, as three essential kinds of road users, pedestrians, bicyclists, and motorcyclists have experienced little to no handling. We explored the influence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.
{"title":"Connected and Autonomous Vehicles (CAVs) Challenges with Nonmotorized Amenities Environments","authors":"Febronie Nambajemariya, Yongshun Wang, Twizerane Jean D’Amour, Kwizera Niyigena Vincent DePaul, Yao Hu","doi":"10.30564/aia.v3i2.3651","DOIUrl":"https://doi.org/10.30564/aia.v3i2.3651","url":null,"abstract":"With the deployment of Connected and Automated Vehicles in the coming decades, road transportation will experience a significant upheaval. CAVs (Connected and Autonomous Vehicles) have been a main emphasis of Transportation and the automotive sector, and the future of transportation system analysis is widely anticipated. The examination and future development of CAVs technology has been the subject of numerous researches. However, as three essential kinds of road users, pedestrians, bicyclists, and motorcyclists have experienced little to no handling. We explored the influence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"32 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88539059","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 : 2021-10-05DOI: 10.14201/adcaij2021103293305
M. Jemmali
This paper aims to find an efficient method to assign different projects to several regions seeking an equitable distribution of the expected revenue of projects. The solutions to this problem are discussed in this paper. This problem is NP-hard. For this work, the constraint is to suppose that all regions have the same socio-economic proprieties. Given a set of regions and a set of projects. Each project is expected to elaborate a fixed revenue. The goal of this paper is to minimize the summation of the total difference between the total revenues of each region and the minimum total revenue assigned to regions. An appropriate schedule of projects is the schedule that ensures an equitable distribution of the total revenues between regions. In this paper, we give a mathematical formulation of the objective function and propose several algorithms to solve the studied problem. An experimental result is presented to discuss the comparison between all implemented algorithms.
{"title":"Projects Distribution Algorithms for Regional Development","authors":"M. Jemmali","doi":"10.14201/adcaij2021103293305","DOIUrl":"https://doi.org/10.14201/adcaij2021103293305","url":null,"abstract":"This paper aims to find an efficient method to assign different projects to several regions seeking an equitable distribution of the expected revenue of projects. The solutions to this problem are discussed in this paper. This problem is NP-hard. For this work, the constraint is to suppose that all regions have the same socio-economic proprieties. Given a set of regions and a set of projects. Each project is expected to elaborate a fixed revenue. The goal of this paper is to minimize the summation of the total difference between the total revenues of each region and the minimum total revenue assigned to regions. An appropriate schedule of projects is the schedule that ensures an equitable distribution of the total revenues between regions. In this paper, we give a mathematical formulation of the objective function and propose several algorithms to solve the studied problem. An experimental result is presented to discuss the comparison between all implemented algorithms.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"11 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90844191","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 : 2021-10-05DOI: 10.14201/adcaij2021103281291
R. Khan, Shadab Siddiqui, Abhishek Rastogi
Women and girls have been subjected to a great deal of violence and harassment in public locations around the country, ranging from stalking to abuse harassment and assault. This research paper examines the role of social media in improving women's safety in Indian cities, with a focus on the use of social media websites and apps such as Twitter, Facebook, and Instagram. This research also looks at how ordinary Indians can develop a sense of responsibility in Indian society so that we can focus on the protection of women in their surroundings. Tweets on the safety of women in Indian cities, which often include images and text as well as written phrases and quotations, can be used to send a message to the Indian youth culture and encourage them to take harsh action and punish those who harass women. Twitter and other Twitter handles that feature hash tag messages are extensively used throughout the world as a channel for women to share their feelings about how they feel when going to work or travelling by public transportation and what is their mental condition when they are surrounded by unknown males, and do they feel safe or not?
{"title":"Crime Detection Using Sentiment Analysis","authors":"R. Khan, Shadab Siddiqui, Abhishek Rastogi","doi":"10.14201/adcaij2021103281291","DOIUrl":"https://doi.org/10.14201/adcaij2021103281291","url":null,"abstract":"Women and girls have been subjected to a great deal of violence and harassment in public locations around the country, ranging from stalking to abuse harassment and assault. This research paper examines the role of social media in improving women's safety in Indian cities, with a focus on the use of social media websites and apps such as Twitter, Facebook, and Instagram. This research also looks at how ordinary Indians can develop a sense of responsibility in Indian society so that we can focus on the protection of women in their surroundings. Tweets on the safety of women in Indian cities, which often include images and text as well as written phrases and quotations, can be used to send a message to the Indian youth culture and encourage them to take harsh action and punish those who harass women. Twitter and other Twitter handles that feature hash tag messages are extensively used throughout the world as a channel for women to share their feelings about how they feel when going to work or travelling by public transportation and what is their mental condition when they are surrounded by unknown males, and do they feel safe or not?","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"196 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79875469","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 : 2021-10-05DOI: 10.14201/adcaij2021103209225
Marcos Antonio de Oliveira, R. Teixeira, R. Sousa, E. Gonçalves
Populational growth increases the number of cars and makes the transport infrastructure increasingly saturated. The control of traffic lights by intelligent software is a promising way to solve the problem caused by this situation. This article addresses this problem that occurs in urban traffic. An agent-based simulation of an urban traffic control system is proposed. The solution is offered as intelligent traffic lights as agents to alleviate traffic congestion at a given location. Each agent controls a crossing and maintains communication with agents from other corners. Thus, they can have greater control of a larger area and identify patterns that can help them to solve congestion problems. The results of our simulated experiments point to the improvement of the urban traffic when using the proposed Multiagent System, in comparison with an approach that uses crossing agents without communication and other that implements static traffic lights.
{"title":"An Agent-Based Simulation to Explore Communication in a System to Control Urban Traffic with Smart Traffic Lights","authors":"Marcos Antonio de Oliveira, R. Teixeira, R. Sousa, E. Gonçalves","doi":"10.14201/adcaij2021103209225","DOIUrl":"https://doi.org/10.14201/adcaij2021103209225","url":null,"abstract":"Populational growth increases the number of cars and makes the transport infrastructure increasingly saturated. The control of traffic lights by intelligent software is a promising way to solve the problem caused by this situation. This article addresses this problem that occurs in urban traffic. An agent-based simulation of an urban traffic control system is proposed. The solution is offered as intelligent traffic lights as agents to alleviate traffic congestion at a given location. Each agent controls a crossing and maintains communication with agents from other corners. Thus, they can have greater control of a larger area and identify patterns that can help them to solve congestion problems. The results of our simulated experiments point to the improvement of the urban traffic when using the proposed Multiagent System, in comparison with an approach that uses crossing agents without communication and other that implements static traffic lights.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"88 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74593808","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：