Pub Date : 2020-07-15DOI: 10.1109/IISA50023.2020.9284365
Yuvaraj Munian, Antonio Martinez-Molina, M. Alamaniotis
Animal Vehicle Collision, commonly called as roadkill, is an emerging threat to humans and wild animals with increasing fatalities every year. Amid Vehicular crashes, animal actions (i.e. deer) are unpredictable and erratic on roadways. This paper unveils a newer dimension for wild animals’ auto-detection during active nocturnal hours using thermal image processing over camera car mount in the vehicle. To implement effective hot spot and moving object detection, obtained radiometric images are transformed and processed by an intelligent system. This intelligent system extracts the features of the image and subsequently detects the existence of an object of interest (i.e. deer). The main technique to extract the features of wild animals is the Histogram of Oriented Gradient (HOG) transform. The features are detected by normalizing the radiometric image and then processed by finding the magnitude and gradient of a pixel. The extracted features are given as an input to the basic deep learning model, a one-dimensional convolutional neural network (1D-CNN), where binary cross-entropy is used to detect the existence of the object. This intelligent system has been tested on a set of real scenarios and gives approximately 91% accuracy in the correct detection of the wild animals on roadsides from the city of San Antonio, TX, in the USA.
{"title":"Intelligent System for Detection of Wild Animals Using HOG and CNN in Automobile Applications","authors":"Yuvaraj Munian, Antonio Martinez-Molina, M. Alamaniotis","doi":"10.1109/IISA50023.2020.9284365","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284365","url":null,"abstract":"Animal Vehicle Collision, commonly called as roadkill, is an emerging threat to humans and wild animals with increasing fatalities every year. Amid Vehicular crashes, animal actions (i.e. deer) are unpredictable and erratic on roadways. This paper unveils a newer dimension for wild animals’ auto-detection during active nocturnal hours using thermal image processing over camera car mount in the vehicle. To implement effective hot spot and moving object detection, obtained radiometric images are transformed and processed by an intelligent system. This intelligent system extracts the features of the image and subsequently detects the existence of an object of interest (i.e. deer). The main technique to extract the features of wild animals is the Histogram of Oriented Gradient (HOG) transform. The features are detected by normalizing the radiometric image and then processed by finding the magnitude and gradient of a pixel. The extracted features are given as an input to the basic deep learning model, a one-dimensional convolutional neural network (1D-CNN), where binary cross-entropy is used to detect the existence of the object. This intelligent system has been tested on a set of real scenarios and gives approximately 91% accuracy in the correct detection of the wild animals on roadsides from the city of San Antonio, TX, in the USA.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116124244","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284396
A. Triantafyllou, G. Tsihrintzis
The paper proposes a new approach to extract facial features via color inverted points. This approach has been incorporated in GRAFFER, a system that we have been developing towards GRoup AFFect Recognition in educational, entertaining and other events. As facial features and especially the eyes, the mouth and the eyebrows offer a lot of information about human emotional states, we investigated existing facial component extraction methods and developed feature extraction through color-inversion of frames-images with facial components.The proposed approach is implemented and tested on the extensive datasets that we have collected with GRAFFER.
{"title":"Group affect Recognition: Facial Feature Extraction via Color Inverted Points","authors":"A. Triantafyllou, G. Tsihrintzis","doi":"10.1109/IISA50023.2020.9284396","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284396","url":null,"abstract":"The paper proposes a new approach to extract facial features via color inverted points. This approach has been incorporated in GRAFFER, a system that we have been developing towards GRoup AFFect Recognition in educational, entertaining and other events. As facial features and especially the eyes, the mouth and the eyebrows offer a lot of information about human emotional states, we investigated existing facial component extraction methods and developed feature extraction through color-inversion of frames-images with facial components.The proposed approach is implemented and tested on the extensive datasets that we have collected with GRAFFER.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131060324","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284347
Eleni S. Vergini, P. Groumpos
Fuzzy Cognitive Maps (FCM) are developed in recent decades in order to model complex systems, combining Fuzzy Logic and Neural Networks. They have been used in various systems and applications and they have been combined with learning algorithms and experts’ knowledge. This paper presents new equations for the concept values calculation and uses the state-space approach in FCMs, separating the concepts as inputs, states and outputs. All the above are applied on a FCM, modelling a Nearly Zero Energy Building (nZEB), creating a strong base for further improvement and research.
{"title":"Advanced Fuzzy Cognitive Maps Modelling a Nearly Zero Energy Building","authors":"Eleni S. Vergini, P. Groumpos","doi":"10.1109/IISA50023.2020.9284347","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284347","url":null,"abstract":"Fuzzy Cognitive Maps (FCM) are developed in recent decades in order to model complex systems, combining Fuzzy Logic and Neural Networks. They have been used in various systems and applications and they have been combined with learning algorithms and experts’ knowledge. This paper presents new equations for the concept values calculation and uses the state-space approach in FCMs, separating the concepts as inputs, states and outputs. All the above are applied on a FCM, modelling a Nearly Zero Energy Building (nZEB), creating a strong base for further improvement and research.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125938212","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284354
Christos Kokkotis, S. Moustakidis, E. Papageorgiou, G. Giakas, D. Tsaopoulos
Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute to the diagnosis of KOA and their impact on model output, with a focus on posthoc explainability. Data were obtained from the osteoarthritis initiative (OAI) database enrolling people, with nonsymptomatic KOA and symptomatic KOA or being at high risk of developing KOA. The current study considered multidisciplinary data from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data from individuals with or without KOA from the baseline visit. For the data mining part, a robust feature selection methodology was employed consisting of filter, wrapper and embedded techniques whereas feature ranking was decided on the basis of a majority vote scheme. The validation of the extracted factors was performed in subgroups employing seven well-known classifiers. A 77.88 % classification accuracy was achieved by Logistic Regression on the group of the first forty selected (40) risk factors. We investigated the behavior of the best model, with respect to classification errors and the impact of used features, to confirm their clinical relevance. The interpretation of the model output was performed by SHAP. The results are the basis for the development of easy-to-use diagnostic tools for clinicians for the early detection of KOA.
{"title":"A Machine Learning workflow for Diagnosis of Knee Osteoarthritis with a focus on post-hoc explainability","authors":"Christos Kokkotis, S. Moustakidis, E. Papageorgiou, G. Giakas, D. Tsaopoulos","doi":"10.1109/IISA50023.2020.9284354","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284354","url":null,"abstract":"Knee Osteoarthritis (KOA) is a multifactorial disease-causing joint pain, deformity and dysfunction. The aim of this paper is to provide a data mining approach that could identify important risk factors which contribute to the diagnosis of KOA and their impact on model output, with a focus on posthoc explainability. Data were obtained from the osteoarthritis initiative (OAI) database enrolling people, with nonsymptomatic KOA and symptomatic KOA or being at high risk of developing KOA. The current study considered multidisciplinary data from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data from individuals with or without KOA from the baseline visit. For the data mining part, a robust feature selection methodology was employed consisting of filter, wrapper and embedded techniques whereas feature ranking was decided on the basis of a majority vote scheme. The validation of the extracted factors was performed in subgroups employing seven well-known classifiers. A 77.88 % classification accuracy was achieved by Logistic Regression on the group of the first forty selected (40) risk factors. We investigated the behavior of the best model, with respect to classification errors and the impact of used features, to confirm their clinical relevance. The interpretation of the model output was performed by SHAP. The results are the basis for the development of easy-to-use diagnostic tools for clinicians for the early detection of KOA.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127070564","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284377
Vassilios Matsouliadis, K. Siountri, D. Vergados
The economic development planning and implementation of the modern societies shall be carried out simultaneously by both environmental protection and a natural resources exploitation in a sustainable way. Building units could achieve these goals directly through an energy design and their corresponding energy efficiency. Initially, a building is being simulated through a computational model, based on parameters of the methodology established by the Energy Efficiency Regulation of Buildings (E.E.R.B.). Afterwards, the results are being thoroughly checked and evaluated in the frame of its energy efficiency, as well as its financial data. The conclusions can be deduced through the exploitation of Internet of Things - IoT technology by evolving the building and thus, expanding the city into a smart city.
{"title":"Analysis and Design of Services in Building Units in the context of a Smart City","authors":"Vassilios Matsouliadis, K. Siountri, D. Vergados","doi":"10.1109/IISA50023.2020.9284377","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284377","url":null,"abstract":"The economic development planning and implementation of the modern societies shall be carried out simultaneously by both environmental protection and a natural resources exploitation in a sustainable way. Building units could achieve these goals directly through an energy design and their corresponding energy efficiency. Initially, a building is being simulated through a computational model, based on parameters of the methodology established by the Energy Efficiency Regulation of Buildings (E.E.R.B.). Afterwards, the results are being thoroughly checked and evaluated in the frame of its energy efficiency, as well as its financial data. The conclusions can be deduced through the exploitation of Internet of Things - IoT technology by evolving the building and thus, expanding the city into a smart city.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114396229","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284379
Antonios Alexos, Christos Kokkotis, S. Moustakidis, E. Papageorgiou, D. Tsaopoulos
Knee Osteoarthritis(KOA) is a serious disease that causes a variety of symptoms, such as severe pain and it is mostly observed in the elder people. The main goal of this study is to build a prognostic tool that will predict the progression of pain in KOA patients using data collected at baseline. In order to do that we leverage a feature importance voting system for identifying the most important risk factors and various machine learning algorithms to classify, whether a patient’s pain with KOA, will stabilize, increase or decrease. These models have been implemented on different combinations of feature subsets, and results up to 84.3% have been achieved with only a small amount of features. The proposed methodology demonstrated unique potential in identifying pain progression at an early stage therefore improving future KOA prevention efforts.
{"title":"Prediction of pain in knee osteoarthritis patients using machine learning: Data from Osteoarthritis Initiative","authors":"Antonios Alexos, Christos Kokkotis, S. Moustakidis, E. Papageorgiou, D. Tsaopoulos","doi":"10.1109/IISA50023.2020.9284379","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284379","url":null,"abstract":"Knee Osteoarthritis(KOA) is a serious disease that causes a variety of symptoms, such as severe pain and it is mostly observed in the elder people. The main goal of this study is to build a prognostic tool that will predict the progression of pain in KOA patients using data collected at baseline. In order to do that we leverage a feature importance voting system for identifying the most important risk factors and various machine learning algorithms to classify, whether a patient’s pain with KOA, will stabilize, increase or decrease. These models have been implemented on different combinations of feature subsets, and results up to 84.3% have been achieved with only a small amount of features. The proposed methodology demonstrated unique potential in identifying pain progression at an early stage therefore improving future KOA prevention efforts.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127880207","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284374
Panteleimon-Evangelos Aivaliotis, F. Grivokostopoulou, I. Perikos, Ioannis Daramouskas, Ioannis Hatziligeroudis
Eye gaze provides indicative information about the status and the behavior of a person and can be very assistive in human-computer interaction. Eye-gaze analysis is very helpful in a variety of applications in order to understand the interest of the users, their behavior or even to unveil distractions. However, the accurate eye-gaze estimation is a very challenging process. In this paper, we present an eye gaze estimation work that relies on convolutional neural networks which imitate the LeNet’s architecture. They analyze eye gaze and provide a 2D vector that concerns the coordinates of the specific pixel inside the 2D screen’s space, in which the user is looking at. Also, a system capable of working under various real-world conditions such as light, angle and distance differentiations was designed and developed. An evaluation study was performed and the results are quite promising pointing out that the system is scalable and accurate in estimating the eye gaze of the users.
{"title":"Eye Gaze Analysis of Students in Educational Systems","authors":"Panteleimon-Evangelos Aivaliotis, F. Grivokostopoulou, I. Perikos, Ioannis Daramouskas, Ioannis Hatziligeroudis","doi":"10.1109/IISA50023.2020.9284374","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284374","url":null,"abstract":"Eye gaze provides indicative information about the status and the behavior of a person and can be very assistive in human-computer interaction. Eye-gaze analysis is very helpful in a variety of applications in order to understand the interest of the users, their behavior or even to unveil distractions. However, the accurate eye-gaze estimation is a very challenging process. In this paper, we present an eye gaze estimation work that relies on convolutional neural networks which imitate the LeNet’s architecture. They analyze eye gaze and provide a 2D vector that concerns the coordinates of the specific pixel inside the 2D screen’s space, in which the user is looking at. Also, a system capable of working under various real-world conditions such as light, angle and distance differentiations was designed and developed. An evaluation study was performed and the results are quite promising pointing out that the system is scalable and accurate in estimating the eye gaze of the users.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123894505","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284408
M. Angelaki, T. Karvounidis, C. Douligeris
This paper proposes an innovative theoretical framework for the development of a persuasive prototype for supporting and evaluating an individual’s traveling attitude through the use of well-designed persuasive smartphone applications for sustainable urban mobility purposes. The newly introduced framework emphasizes the importance of engaging and committing users to improve their travel habits through continuous motivation and evaluation of both how their traveling habits affect the environment and how they contribute to better future mobility planning decisions. It consists of five stages; it takes into consideration various elements from existing frameworks and introduces a critical stage, that of the users’ rewarded assessment/feedback, based on the daily evaluation of the application effectiveness to persuade the user to move in an eco-friendlier way. For each stage of the proposed framework, a detailed description of the strategies that could be applied, and the related target group is provided.
{"title":"A Theoretical Persuasive Framework for Supporting and Evolving an Individual’s Sustainable Mobile Traveling Attitude","authors":"M. Angelaki, T. Karvounidis, C. Douligeris","doi":"10.1109/IISA50023.2020.9284408","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284408","url":null,"abstract":"This paper proposes an innovative theoretical framework for the development of a persuasive prototype for supporting and evaluating an individual’s traveling attitude through the use of well-designed persuasive smartphone applications for sustainable urban mobility purposes. The newly introduced framework emphasizes the importance of engaging and committing users to improve their travel habits through continuous motivation and evaluation of both how their traveling habits affect the environment and how they contribute to better future mobility planning decisions. It consists of five stages; it takes into consideration various elements from existing frameworks and introduces a critical stage, that of the users’ rewarded assessment/feedback, based on the daily evaluation of the application effectiveness to persuade the user to move in an eco-friendlier way. For each stage of the proposed framework, a detailed description of the strategies that could be applied, and the related target group is provided.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129168945","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284391
Ioannis Batsis, D. Bargiotas, Aspassia Daskalopulu
The Inverter is a DC/AC type Power Converter and is an important part of Electrical Power Networks because several energy source outputs are in DC form. In the mid-80s, the Multilevel Inverter (MLI) was developed. Its output is a staircase waveform, which approximates the sinusoidal waveform of the Power Network. Approximation improves as the number of stairs (levels) increases. But due to practical difficulties, concerning the circuit layout and the implementation of a suitable control strategy for the switching elements, eleven levels output is a common practice. Research interest around MLIs abounds due to the advantages they offer compared to classical DC/AC Inverters. MLIs may lead to new circuit layouts with fewer components, novel and more effective control strategies for the switching elements and to the development of new modulation methods for calculating the time of activation and deactivation of the switching elements. In this paper we propose a new modulation method based on the technique of Mathematical optimization.
{"title":"Switching angles calculation through Mathematical optimization in Multilevel Inverters","authors":"Ioannis Batsis, D. Bargiotas, Aspassia Daskalopulu","doi":"10.1109/IISA50023.2020.9284391","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284391","url":null,"abstract":"The Inverter is a DC/AC type Power Converter and is an important part of Electrical Power Networks because several energy source outputs are in DC form. In the mid-80s, the Multilevel Inverter (MLI) was developed. Its output is a staircase waveform, which approximates the sinusoidal waveform of the Power Network. Approximation improves as the number of stairs (levels) increases. But due to practical difficulties, concerning the circuit layout and the implementation of a suitable control strategy for the switching elements, eleven levels output is a common practice. Research interest around MLIs abounds due to the advantages they offer compared to classical DC/AC Inverters. MLIs may lead to new circuit layouts with fewer components, novel and more effective control strategies for the switching elements and to the development of new modulation methods for calculating the time of activation and deactivation of the switching elements. In this paper we propose a new modulation method based on the technique of Mathematical optimization.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115445065","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 : 2020-07-15DOI: 10.1109/IISA50023.2020.9284409
O. Shabalina, V. Guriev, Stanislav Kosyakov, Nikita Dmitriev, A. Davtian
Many people with intellectual disabilities (PID) can experience problems in different aspects of their everyday life. The capabilities of modern mobile applications offer promising opportunities to help PID to cope with their everyday life problems and to feel more confident in everyday activities. Mobile application development is one of the most fast-moving industries and to support the mobile applications development process special software tools are designed. However, the development of mobile applications for PID has its own specifics due to capabilities and limitations of this category of users. The article presents a mobile application development management (MAMD) system, that takes into account these specifics and covers both the stage of the development of mobile applications for PID and the stage of their usage. The system supports modular development of mobile applications based on reusing software components. The system allows to conFigure the interface of mobile applications and adapt it to the users with different capabilities and limitations. The system is universal in terms of platform selection, so the developers of mobile applications for PID can always chose the most suitable platform for different categories of PID.
{"title":"MADM System for the Development of Adaptable Mobile Applications for People with Intellectual Disabilities","authors":"O. Shabalina, V. Guriev, Stanislav Kosyakov, Nikita Dmitriev, A. Davtian","doi":"10.1109/IISA50023.2020.9284409","DOIUrl":"https://doi.org/10.1109/IISA50023.2020.9284409","url":null,"abstract":"Many people with intellectual disabilities (PID) can experience problems in different aspects of their everyday life. The capabilities of modern mobile applications offer promising opportunities to help PID to cope with their everyday life problems and to feel more confident in everyday activities. Mobile application development is one of the most fast-moving industries and to support the mobile applications development process special software tools are designed. However, the development of mobile applications for PID has its own specifics due to capabilities and limitations of this category of users. The article presents a mobile application development management (MAMD) system, that takes into account these specifics and covers both the stage of the development of mobile applications for PID and the stage of their usage. The system supports modular development of mobile applications based on reusing software components. The system allows to conFigure the interface of mobile applications and adapt it to the users with different capabilities and limitations. The system is universal in terms of platform selection, so the developers of mobile applications for PID can always chose the most suitable platform for different categories of PID.","PeriodicalId":109238,"journal":{"name":"2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126948096","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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