Pub Date : 2019-10-01DOI: 10.1109/ISC246665.2019.9071688
M. Kuhail, Manohar Boorlu, Neeraj Padarthi, Collin Rottinghaus
Parking is increasingly an issue in the world today especially in large and growing cities with contemporary urban mobility. The effort spent in searching for available parking spots results in significant loss of resources such as time, and fuel, as well as environmental pollution. Parking Availability can be influenced by many factors such as time of day, day of week, location, nearby events, weather and traffic conditions. Driven by the idea of predicting parking availability to help drivers plan ahead of time, we contribute a Parking Availability Forecasting Model, which uses a time-series analysis and machine-learning algorithms to predict the number of available parking spots at a certain location on a desired date and time. The forecasting model is trained on historical parking data from the cities of Kansas City, US and Melbourne, Australia. This paper also compares the accuracy of different time-series forecasting models, and how each of them fits our use-case scenario. Multivariate data analysis together with temperature and weather summary are used to cross-validate our forecasting model.
{"title":"Parking Availability Forecasting Model","authors":"M. Kuhail, Manohar Boorlu, Neeraj Padarthi, Collin Rottinghaus","doi":"10.1109/ISC246665.2019.9071688","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071688","url":null,"abstract":"Parking is increasingly an issue in the world today especially in large and growing cities with contemporary urban mobility. The effort spent in searching for available parking spots results in significant loss of resources such as time, and fuel, as well as environmental pollution. Parking Availability can be influenced by many factors such as time of day, day of week, location, nearby events, weather and traffic conditions. Driven by the idea of predicting parking availability to help drivers plan ahead of time, we contribute a Parking Availability Forecasting Model, which uses a time-series analysis and machine-learning algorithms to predict the number of available parking spots at a certain location on a desired date and time. The forecasting model is trained on historical parking data from the cities of Kansas City, US and Melbourne, Australia. This paper also compares the accuracy of different time-series forecasting models, and how each of them fits our use-case scenario. Multivariate data analysis together with temperature and weather summary are used to cross-validate our forecasting model.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125773448","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071716
I. Kallel, Malak Chniter
Building Collaborative e-Learning Teams in a Smart Education Environment Abstract: Collaborative learning is a teaching method requiring to group students according to various ways and types, it is the most used approaches to arrange students for an effective learning in the classroom. However, with the large number of students in e-learning context, and their different settings, the task to establish cooperation and collaboration among them becomes harder and complex. Hence, several researchers proposed different approaches to overcome this diversity and find the best solution for building a good collaborative learning environment. In this context, this work discuss the Ant Colony optimisation (ACO) bio-inspired approaches and their added values in Data Analysis which leads us to assume the collaborative e-learning teams (CeLT) formation as a data clustering problem. Therefore, we analyse and discuss some ant clustering algorithms, and adapt the best one so it can be applied on an educational data sets. The results show a good starting point for future works on a real smart education Environment.
{"title":"Building Collaborative e-Learning Teams in a Smart Education Environment","authors":"I. Kallel, Malak Chniter","doi":"10.1109/ISC246665.2019.9071716","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071716","url":null,"abstract":"Building Collaborative e-Learning Teams in a Smart Education Environment Abstract: Collaborative learning is a teaching method requiring to group students according to various ways and types, it is the most used approaches to arrange students for an effective learning in the classroom. However, with the large number of students in e-learning context, and their different settings, the task to establish cooperation and collaboration among them becomes harder and complex. Hence, several researchers proposed different approaches to overcome this diversity and find the best solution for building a good collaborative learning environment. In this context, this work discuss the Ant Colony optimisation (ACO) bio-inspired approaches and their added values in Data Analysis which leads us to assume the collaborative e-learning teams (CeLT) formation as a data clustering problem. Therefore, we analyse and discuss some ant clustering algorithms, and adapt the best one so it can be applied on an educational data sets. The results show a good starting point for future works on a real smart education Environment.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115302593","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071749
Abeer Hussein Salem, O. Mahmoud
This paper is highlighting the importance of textile waste management nowadays, and clarifying the current status of textile waste recycling in different countries by explaining two successful benchmarks. As well as, it is proposing a prototype that includes solutions for textile waste by circular economy knowledge -using a material recovery approach, the applied processes in Mei Chin, Queenstown, Singapore, which can be implemented in reality. Throughout these paper authors proposing an organization that is responsible for applying all material recovery phases after using the circular economy- in the textile sector which are the collection, sorting, recycling phases. As well as identifying the needed material flows for all textile management processes which are generally included in a one business model that can be applied in any city with simple modifications according to the city identity, conditions, and applicable laws in the city.
{"title":"Proposing applied processes to achieve the Circular Economy model in the textile sector A case study in Mei Chin, Queenstown, Singapore","authors":"Abeer Hussein Salem, O. Mahmoud","doi":"10.1109/ISC246665.2019.9071749","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071749","url":null,"abstract":"This paper is highlighting the importance of textile waste management nowadays, and clarifying the current status of textile waste recycling in different countries by explaining two successful benchmarks. As well as, it is proposing a prototype that includes solutions for textile waste by circular economy knowledge -using a material recovery approach, the applied processes in Mei Chin, Queenstown, Singapore, which can be implemented in reality. Throughout these paper authors proposing an organization that is responsible for applying all material recovery phases after using the circular economy- in the textile sector which are the collection, sorting, recycling phases. As well as identifying the needed material flows for all textile management processes which are generally included in a one business model that can be applied in any city with simple modifications according to the city identity, conditions, and applicable laws in the city.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116498266","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071677
Leila Bourrich, S. L. Elhaq
the pick-up of parcels presents the upstream link of the urban freight transportation chain, it is based on pick-up routes by responding to customer requests. Each transport company has its own management model. For this link, whose main objective is to maximize pick-up requests and satisfy a very demanding clientele. In Morocco, this process knows a large number of difficulties due to several criteria linked to customers satisfaction’s factors. These difficulties make the transportation companies losing every year an important rate of their customers. In literature, this problem has not been studied before, for this reason our objective is the proposition of an efficient model that helps to know a global vision on the customer’s behavior in relationship with this kind of transportation. Then the prediction of the pick-up requests for each customer during a specific period to finally optimize the routing vehicle problem associated to our case study. In this paper, we explore the effect of the classification of customers per categories, by using the K-means clustering method. This solution will help to group our dataset composed by 256 customers from Moroccan Transportation Company. Where their kind of demand is intermittent. This classification is into N clusters with specific characterizes. Thanks to this method, we find as a result that the number of cluster K=4 is the optimal classification of our dataset and their gathering by cluster which each one had specific characteristics.
{"title":"Customers’ classification for pick-up’s demand by using the K-means clustering: A case study of urban freight transportation in Casablanca city","authors":"Leila Bourrich, S. L. Elhaq","doi":"10.1109/ISC246665.2019.9071677","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071677","url":null,"abstract":"the pick-up of parcels presents the upstream link of the urban freight transportation chain, it is based on pick-up routes by responding to customer requests. Each transport company has its own management model. For this link, whose main objective is to maximize pick-up requests and satisfy a very demanding clientele. In Morocco, this process knows a large number of difficulties due to several criteria linked to customers satisfaction’s factors. These difficulties make the transportation companies losing every year an important rate of their customers. In literature, this problem has not been studied before, for this reason our objective is the proposition of an efficient model that helps to know a global vision on the customer’s behavior in relationship with this kind of transportation. Then the prediction of the pick-up requests for each customer during a specific period to finally optimize the routing vehicle problem associated to our case study. In this paper, we explore the effect of the classification of customers per categories, by using the K-means clustering method. This solution will help to group our dataset composed by 256 customers from Moroccan Transportation Company. Where their kind of demand is intermittent. This classification is into N clusters with specific characterizes. Thanks to this method, we find as a result that the number of cluster K=4 is the optimal classification of our dataset and their gathering by cluster which each one had specific characteristics.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115060346","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071759
Muhammad Usman, Kashif Ahmad, M. Qaraqe
The concerns for a healthier diet are increasing day by day, especially in diabetics wherein the aim of healthier diet can only be achieved by keeping a track of daily food intake and glucose-level. As a consequence, there is an ever-increasing need of automatic tools able to help diabetics to manage their diet and also help physicians to better analyze the effects of various types of food on the glucose-level of diabetics. In this paper, we propose an intelligent food recognition and tracking system for diabetics, which is potentially an essential part of a mobile application that we propose to couple food intake with the blood glucose-level using glucose measuring sensors. Being an essential component of the application, for food recognition we rely on several feature extraction and classification techniques individually and jointly utilized using an early and two different late fusion techniques, namely (i) Particle Swarm Optimization (PSO) based fusion and (iii) simple averaging. Moreover, we also evaluate the performance of several deep features. In addition, we collect a large-scale dataset containing images from several types of local Middle-Eastern food, which is intended to become a powerful support tool for future research in the domain.
{"title":"A Food Recognition and Tracking System for Diabetics in the Middle East","authors":"Muhammad Usman, Kashif Ahmad, M. Qaraqe","doi":"10.1109/ISC246665.2019.9071759","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071759","url":null,"abstract":"The concerns for a healthier diet are increasing day by day, especially in diabetics wherein the aim of healthier diet can only be achieved by keeping a track of daily food intake and glucose-level. As a consequence, there is an ever-increasing need of automatic tools able to help diabetics to manage their diet and also help physicians to better analyze the effects of various types of food on the glucose-level of diabetics. In this paper, we propose an intelligent food recognition and tracking system for diabetics, which is potentially an essential part of a mobile application that we propose to couple food intake with the blood glucose-level using glucose measuring sensors. Being an essential component of the application, for food recognition we rely on several feature extraction and classification techniques individually and jointly utilized using an early and two different late fusion techniques, namely (i) Particle Swarm Optimization (PSO) based fusion and (iii) simple averaging. Moreover, we also evaluate the performance of several deep features. In addition, we collect a large-scale dataset containing images from several types of local Middle-Eastern food, which is intended to become a powerful support tool for future research in the domain.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130013953","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071668
Hajar Baghcheband, Zafeiris Kokkinogenis, R. Rossetti
Traffic congestion is an issue regarding the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and make different decisions towards transportation modes. Modification and improvement of these systems affect the commuters’ perspective and social welfare. In this study, the effect of road flow equilibrium on commuters’ utilities with different types of transportation modes will be discussed. A simple network with two modes of transportation will be illustrated and three different cost policies were considered to test the efficiency of reinforcement learning in commuters’ daily trip decision-making regarding time and mode. The artificial society of agents is simulated to analyse the results.
{"title":"Transportation Policy Evaluation Using Minority Games and Agent-Based Simulation","authors":"Hajar Baghcheband, Zafeiris Kokkinogenis, R. Rossetti","doi":"10.1109/ISC246665.2019.9071668","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071668","url":null,"abstract":"Traffic congestion is an issue regarding the vitality of cities and the welfare of citizens. Transportation systems are using various technologies to allow users to adapt and make different decisions towards transportation modes. Modification and improvement of these systems affect the commuters’ perspective and social welfare. In this study, the effect of road flow equilibrium on commuters’ utilities with different types of transportation modes will be discussed. A simple network with two modes of transportation will be illustrated and three different cost policies were considered to test the efficiency of reinforcement learning in commuters’ daily trip decision-making regarding time and mode. The artificial society of agents is simulated to analyse the results.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124320829","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071686
Arthur Souza, Larysse Izidio, Aluizio F. Rocha Neto, N. Cacho, T. Batista
In the Smart Cities context, a plethora of Middleware Platforms has been proposed to support applications execution and data processing. However, just a few of them have explored the overall Smart Cities computing environment. The vast majority focuses on specific domains, typically presenting a sensor-acquisition architecture for processing in Cloud Computing. Most recent initiatives try to include Cloud Computing and Edge Computing, while few of them use the three computing levels (Cloud, Fog, and Edge). Besides, many of these platforms do not define the services that should be deployed at each level, nor how the developer can better use each feature. This work fulfills this gap presenting an architecture for applications classifying services implemented by a typical Computing Environment of Smart Cities. Our architecture uses all the computational levels (Cloud, Fog, Edge) of a city infrastructure, and it defines how to deploy each type of service at each level. We also present an example of the proposed architecture that we are implementing in the city of Natal, where some evaluative tests have been carried out.
{"title":"Sapparchi: An Architecture for Smart City Applications from Edge, Fog and Cloud Computing","authors":"Arthur Souza, Larysse Izidio, Aluizio F. Rocha Neto, N. Cacho, T. Batista","doi":"10.1109/ISC246665.2019.9071686","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071686","url":null,"abstract":"In the Smart Cities context, a plethora of Middleware Platforms has been proposed to support applications execution and data processing. However, just a few of them have explored the overall Smart Cities computing environment. The vast majority focuses on specific domains, typically presenting a sensor-acquisition architecture for processing in Cloud Computing. Most recent initiatives try to include Cloud Computing and Edge Computing, while few of them use the three computing levels (Cloud, Fog, and Edge). Besides, many of these platforms do not define the services that should be deployed at each level, nor how the developer can better use each feature. This work fulfills this gap presenting an architecture for applications classifying services implemented by a typical Computing Environment of Smart Cities. Our architecture uses all the computational levels (Cloud, Fog, Edge) of a city infrastructure, and it defines how to deploy each type of service at each level. We also present an example of the proposed architecture that we are implementing in the city of Natal, where some evaluative tests have been carried out.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"121 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120872054","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071666
R. Naidoo, Marnó Zietsman
By placing optimally sized, selected and placed clusters of renewable based microgrids in low voltage radial distribution networks emergent energy demands are met. However, this leads to fundamental changes and challenges in the topology and protection coordination performance, due to the increased dynamic fault currents, complex operation and control of the embedded microgrid. With the use of an optimization algorithm along with communication and sequence component based relays with existing protection methods, effective microgrid adaptability and protection coordination in the radial distribution network is achieved. An adaptive hybrid protection philosophy and optimization function is used to assist in maintaining the protection coordination. This improves the protection selectivity, sensitivity, stability and reliability of the existing radial low voltage distribution network when protection adaptable renewable based microgrids are integrated.
{"title":"An Adaptive Protection Philosophy for Embedded Renewable Distributed Generators in Low Voltage Radial Networks","authors":"R. Naidoo, Marnó Zietsman","doi":"10.1109/ISC246665.2019.9071666","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071666","url":null,"abstract":"By placing optimally sized, selected and placed clusters of renewable based microgrids in low voltage radial distribution networks emergent energy demands are met. However, this leads to fundamental changes and challenges in the topology and protection coordination performance, due to the increased dynamic fault currents, complex operation and control of the embedded microgrid. With the use of an optimization algorithm along with communication and sequence component based relays with existing protection methods, effective microgrid adaptability and protection coordination in the radial distribution network is achieved. An adaptive hybrid protection philosophy and optimization function is used to assist in maintaining the protection coordination. This improves the protection selectivity, sensitivity, stability and reliability of the existing radial low voltage distribution network when protection adaptable renewable based microgrids are integrated.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131843295","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071671
T. Chaiwattanayon, N. Oudomying, P. Sankosik, P. A-Aree, K. Vasiksiri, N. Boonyakitjakarn, T. Janwattanakul, T. Pruekkumvong, P. Ketprapakorn, B. Lohachitranont, P. Wiboontanasarn, C. Ratanamahatana, N. Prompoon, M. Pipattanasomporn
In a university campus, it is often the case that students forget their important items, such as calculators, laptops or laptop chargers, on an exam day. Students need to borrow such items, but they do not know whom to borrow from. Buying a new calculator, or a laptop charger is expensive, considering that they are in need for only a couple of hours. Hence, the team proposes to develop ‘Share-IT’–a sharing platform for a smart campus that matches borrowers’ requests to available items by lenders. This paper discusses the developed Share-IT app, its functional and non-functional requirements, its underlying hardware and software components, and the entire development process, including system architecture, data model and software quality assurance tests and results. While the Share-IT prototype has been demonstrated at a university campus in Thailand, the software development process and method as reported in this paper can be implemented in other smart campus elsewhere.
{"title":"Share-IT: A Sharing Platform for a Smart Campus","authors":"T. Chaiwattanayon, N. Oudomying, P. Sankosik, P. A-Aree, K. Vasiksiri, N. Boonyakitjakarn, T. Janwattanakul, T. Pruekkumvong, P. Ketprapakorn, B. Lohachitranont, P. Wiboontanasarn, C. Ratanamahatana, N. Prompoon, M. Pipattanasomporn","doi":"10.1109/ISC246665.2019.9071671","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071671","url":null,"abstract":"In a university campus, it is often the case that students forget their important items, such as calculators, laptops or laptop chargers, on an exam day. Students need to borrow such items, but they do not know whom to borrow from. Buying a new calculator, or a laptop charger is expensive, considering that they are in need for only a couple of hours. Hence, the team proposes to develop ‘Share-IT’–a sharing platform for a smart campus that matches borrowers’ requests to available items by lenders. This paper discusses the developed Share-IT app, its functional and non-functional requirements, its underlying hardware and software components, and the entire development process, including system architecture, data model and software quality assurance tests and results. While the Share-IT prototype has been demonstrated at a university campus in Thailand, the software development process and method as reported in this paper can be implemented in other smart campus elsewhere.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129168928","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 : 2019-10-01DOI: 10.1109/ISC246665.2019.9071687
Hanwen Li, Zhenkai Yang, Yun Wang, Li Yao, Xiao-jie Zhao
The assessment of psychological health is an important part of the construction of smart city. In order to establish psychological crisis discrimination criterion and analyze the contribution of each factor, this study uses machine learning algorithms based on big scale data collected during the construction of smart cities. Firstly, the psychological crisis level is discriminated by using ISOMAP algorithm and K-means algorithm. Then the logistic regression is modeled to estimate the influence coefficient of each factor. Finally, the above models were used to assess each individual's psychological states. The results show that the classification performance of machine learning algorithm is better than that of the traditional norm. Moreover, the coefficients obtained by logistic regression can be used to represent the contribution of various factors to individual's psychological state.
{"title":"Construction of Psychological Crisis Assessment Model Based on Machine Learning","authors":"Hanwen Li, Zhenkai Yang, Yun Wang, Li Yao, Xiao-jie Zhao","doi":"10.1109/ISC246665.2019.9071687","DOIUrl":"https://doi.org/10.1109/ISC246665.2019.9071687","url":null,"abstract":"The assessment of psychological health is an important part of the construction of smart city. In order to establish psychological crisis discrimination criterion and analyze the contribution of each factor, this study uses machine learning algorithms based on big scale data collected during the construction of smart cities. Firstly, the psychological crisis level is discriminated by using ISOMAP algorithm and K-means algorithm. Then the logistic regression is modeled to estimate the influence coefficient of each factor. Finally, the above models were used to assess each individual's psychological states. The results show that the classification performance of machine learning algorithm is better than that of the traditional norm. Moreover, the coefficients obtained by logistic regression can be used to represent the contribution of various factors to individual's psychological state.","PeriodicalId":306836,"journal":{"name":"2019 IEEE International Smart Cities Conference (ISC2)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133318752","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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