Pub Date : 2018-09-01DOI: 10.1109/ICDIM.2018.8847072
Shantanu Deshmukh, Natalia Khuri
Many drugs prescribed to pediatric patients were never tested for use in children. Yet, there are multiple physiological and anatomical differences that lead to variations in therapeutic response and adverse side effects in children compared with adults. While some information about pediatric drug response is available on the World Wide Web, it is often disseminated in form of unstructured texts or web sites with limited analytics capabilities. In this work, we prototyped a data analytics platform called PediatricDB, to address an unmet need of integrating public data on safety and efficacy of drugs in pediatric patients. Our web portal provides a seamless access to these assessments for prescribers, drug developers, regulators, and researchers.
{"title":"PediatricDB: Data Analytics Platform for Pediatric Healthcare","authors":"Shantanu Deshmukh, Natalia Khuri","doi":"10.1109/ICDIM.2018.8847072","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8847072","url":null,"abstract":"Many drugs prescribed to pediatric patients were never tested for use in children. Yet, there are multiple physiological and anatomical differences that lead to variations in therapeutic response and adverse side effects in children compared with adults. While some information about pediatric drug response is available on the World Wide Web, it is often disseminated in form of unstructured texts or web sites with limited analytics capabilities. In this work, we prototyped a data analytics platform called PediatricDB, to address an unmet need of integrating public data on safety and efficacy of drugs in pediatric patients. Our web portal provides a seamless access to these assessments for prescribers, drug developers, regulators, and researchers.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"41 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138053","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8847135
Rami Ibrahim, M. O. Shafiq
In past years, trajectory data generated from Automatic Identification System (AIS) networks and taxi GPS devices increased significantly. There is a high demand for analyzing this data and extracting the knowledge from it. Large-scale taxi trajectory data is represented by a sequence of timestamped geographical locations, this sequence starts with the origin point and ends with the destination point. Applying data mining techniques such as clustering on trajectory data can provide useful information about the movement patterns and the behavior of people. Thus, can enhance the transportation management services in terms of urban planning and environment issues. In this paper, we propose a methodology which extracts movement patterns of taxi trips in Porto, Portugal. we cluster taxi trips using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, each point in the trip is a pair of coordinates which consists of longitude and latitude values.
{"title":"Mining Trajectory Data and Identifying Patterns for Taxi Movement Trips","authors":"Rami Ibrahim, M. O. Shafiq","doi":"10.1109/ICDIM.2018.8847135","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8847135","url":null,"abstract":"In past years, trajectory data generated from Automatic Identification System (AIS) networks and taxi GPS devices increased significantly. There is a high demand for analyzing this data and extracting the knowledge from it. Large-scale taxi trajectory data is represented by a sequence of timestamped geographical locations, this sequence starts with the origin point and ends with the destination point. Applying data mining techniques such as clustering on trajectory data can provide useful information about the movement patterns and the behavior of people. Thus, can enhance the transportation management services in terms of urban planning and environment issues. In this paper, we propose a methodology which extracts movement patterns of taxi trips in Porto, Portugal. we cluster taxi trips using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, each point in the trip is a pair of coordinates which consists of longitude and latitude values.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461090","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8846968
P. Pichappan, P. Vijayakumar
Measuring the value and quality of web page is a challenging issue. Research has produced many varying standards and the absence of a global framework is evident. In the current work we generated a few quality measures for judging the quality of web page content. We limit our exercise to the content quality and refrain from other web page features.
{"title":"Towards scalable standards for web content usability","authors":"P. Pichappan, P. Vijayakumar","doi":"10.1109/ICDIM.2018.8846968","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8846968","url":null,"abstract":"Measuring the value and quality of web page is a challenging issue. Research has produced many varying standards and the absence of a global framework is evident. In the current work we generated a few quality measures for judging the quality of web page content. We limit our exercise to the content quality and refrain from other web page features.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114458245","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8846973
Mohamed H. Al-Meer, M. Mamun
This paper presents a deep feed-forward neural network classifier to automatically classify the stages of sleep using raw data taken from a single electropalatogram channel (Fpz-Cz). No features are extracted at all from the data, and the network can classify the five sleep stages: waking, Nl, N2, N3, N4, and rapid eye movement. The network has three layers, takes as an input a l-s epochs to be classified, and requires no signal pre-processing nor feature extraction. We trained and evaluated our system using DeepLearning4J, the free Java framework for test data taken from PhysioNet’s Polysomnography Sleep database. An accuracy of 0.99 within a constrained environment has been reached.
{"title":"Deep Learning in Classifying Sleep Stages","authors":"Mohamed H. Al-Meer, M. Mamun","doi":"10.1109/ICDIM.2018.8846973","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8846973","url":null,"abstract":"This paper presents a deep feed-forward neural network classifier to automatically classify the stages of sleep using raw data taken from a single electropalatogram channel (Fpz-Cz). No features are extracted at all from the data, and the network can classify the five sleep stages: waking, Nl, N2, N3, N4, and rapid eye movement. The network has three layers, takes as an input a l-s epochs to be classified, and requires no signal pre-processing nor feature extraction. We trained and evaluated our system using DeepLearning4J, the free Java framework for test data taken from PhysioNet’s Polysomnography Sleep database. An accuracy of 0.99 within a constrained environment has been reached.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128382428","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}
Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. One of the characteristic hallmarks of ASD is the difficulty of making or maintaining eye contact. In this respect, the eye-tracking technology has come into prominence to support the study and analysis of autism. This paper develops a methodology to visualize the eye-tracking patterns of ASD-diagnosed individuals with particular focus on children at early stages of development. The key idea is to transform the dynamics of eye motion into a visual representation, and hence diagnosis-related tasks could be approached using image-based techniques. The visualizations produced are made publicly available in an image dataset to be used by other studies aiming to experiment the potentials of eye-tracking within the ASD context. It is believed that the dataset can allow for developing further useful applications or discovering interesting insights using Machine Learning or data mining techniques
{"title":"Visualization of Eye-Tracking Patterns in Autism Spectrum Disorder: Method and Dataset","authors":"Romuald Carette, Mahmoud Elbattah, Gilles Dequen, Jean-Luc Guérin, Federica Cilia","doi":"10.1109/ICDIM.2018.8846967","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8846967","url":null,"abstract":"Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. One of the characteristic hallmarks of ASD is the difficulty of making or maintaining eye contact. In this respect, the eye-tracking technology has come into prominence to support the study and analysis of autism. This paper develops a methodology to visualize the eye-tracking patterns of ASD-diagnosed individuals with particular focus on children at early stages of development. The key idea is to transform the dynamics of eye motion into a visual representation, and hence diagnosis-related tasks could be approached using image-based techniques. The visualizations produced are made publicly available in an image dataset to be used by other studies aiming to experiment the potentials of eye-tracking within the ASD context. It is believed that the dataset can allow for developing further useful applications or discovering interesting insights using Machine Learning or data mining techniques","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121760338","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8846982
Michael George
Topic modelling is an approach in data mining, use machine learning methods to discover patterns in large amount of unstructured text. It takes a collection of documents and group the words into clusters of words that we call Bag of words, and identify topics by using process of similarity. Topic modelling provides us with methods to organize, understand and summarize large collections of textual information. There are a lot of approaches have been exposed for Topic modelling, the most in use are Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and explicit semantic analysis (ESA). In our study we describing an approach to refine Topic detection based on 2d vector space model VSM by using Apriori algorithm along with Natural language processing, to form a better connected terms in vector space for clean engagement with the query.
{"title":"Unsupervised Topic Detection based on 2D Vector Space model using Apriori Algorithm and NLP","authors":"Michael George","doi":"10.1109/ICDIM.2018.8846982","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8846982","url":null,"abstract":"Topic modelling is an approach in data mining, use machine learning methods to discover patterns in large amount of unstructured text. It takes a collection of documents and group the words into clusters of words that we call Bag of words, and identify topics by using process of similarity. Topic modelling provides us with methods to organize, understand and summarize large collections of textual information. There are a lot of approaches have been exposed for Topic modelling, the most in use are Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and explicit semantic analysis (ESA). In our study we describing an approach to refine Topic detection based on 2d vector space model VSM by using Apriori algorithm along with Natural language processing, to form a better connected terms in vector space for clean engagement with the query.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114951759","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8847156
Fadi Yamout, Rachad Lakkis
In information retrieval, documents are usually retrieved using lexical matching which matches where words in a user's query with words found in a set of documents. A significant model used in information retrieval is the vector space model where these words are represented as a vector in space and are assigned weights using a favorite weighting technique called TFIDF (Term Frequency Inverse Document Frequency). In this thesis, we have devised three new weighting techniques to improve the TFIDF weighting technique. The first technique is Dispersed Words Weight Augmentation (DWWA) which gives more weight to the words distributed in most of the document’s paragraphs; we consider that those words are more significant than words found in few paragraphs. The second technique is called Title Weight Augmentation (TWA) which gives more weight to the words found in the document’s title and first paragraphs. The third technique is called First Ranked Words Weight Augmentation (FRWWA) which increments further the weight of the most frequent words in a document. We tested the three techniques, and we found more relevant documents were retrieved in our system.
在信息检索中，通常使用词汇匹配来检索文档，它将用户查询中的单词与一组文档中找到的单词进行匹配。信息检索中使用的一个重要模型是向量空间模型，其中这些词被表示为空间中的向量，并使用称为TFIDF (Term Frequency Inverse Document Frequency)的最喜欢的加权技术分配权重。在本文中，我们设计了三种新的加权技术来改进TFIDF加权技术。第一种技术是分散词权增强(DWWA)，它赋予分布在大多数文档段落中的词更多的权重;我们认为，这些词比在少数段落中发现的词更有意义。第二种技术被称为标题权重增强(TWA)，它赋予文档标题和第一段中的单词更多权重。第三种技术被称为第一排名单词权重增强(FRWWA)，它进一步增加文档中最频繁单词的权重。我们测试了这三种技术，我们发现在我们的系统中检索到更多相关的文档。
{"title":"Improved TFIDF weighting techniques in document Retrieval","authors":"Fadi Yamout, Rachad Lakkis","doi":"10.1109/ICDIM.2018.8847156","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8847156","url":null,"abstract":"In information retrieval, documents are usually retrieved using lexical matching which matches where words in a user's query with words found in a set of documents. A significant model used in information retrieval is the vector space model where these words are represented as a vector in space and are assigned weights using a favorite weighting technique called TFIDF (Term Frequency Inverse Document Frequency). In this thesis, we have devised three new weighting techniques to improve the TFIDF weighting technique. The first technique is Dispersed Words Weight Augmentation (DWWA) which gives more weight to the words distributed in most of the document’s paragraphs; we consider that those words are more significant than words found in few paragraphs. The second technique is called Title Weight Augmentation (TWA) which gives more weight to the words found in the document’s title and first paragraphs. The third technique is called First Ranked Words Weight Augmentation (FRWWA) which increments further the weight of the most frequent words in a document. We tested the three techniques, and we found more relevant documents were retrieved in our system.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116005594","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8847005
Jolanta Mizera-Pietraszko
Nowadays, communication between patient and doctor during an appointment has changed significantly owning to the opportunity that medical portals provide. Whether or not necessarily appreciated by the doctors, the patients became more aware of the first symptoms’ suggesting a particular disease and the medical procedures that apply as a standard. Estimating amount of reliable factual medical information in a document is carried out by parametrizing space of digital documents and dividing it into subsequent layers that represent distribution of the system responses computed as random variables to a query about medical information. Analyzed are the following attributes: dynamism of decrease of query words numbers in the documents, precision, recall in the metric space layers, their mutual correlation and specifically the amount of reliable medical information in the documents. Sensitivity of estimators is explored in order to determine the final decision about further browsing digital documents of the metric space for more medical information that satisfies the user’s need. For identification of the true positive information in the space layer and then, in each document of this layer, matching of medical terminology with the document contents, is processed following binary Boolean search space model.
{"title":"Sensitivity of Estimators for Measuring Information Amount in Web-Based Medical Documents","authors":"Jolanta Mizera-Pietraszko","doi":"10.1109/ICDIM.2018.8847005","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8847005","url":null,"abstract":"Nowadays, communication between patient and doctor during an appointment has changed significantly owning to the opportunity that medical portals provide. Whether or not necessarily appreciated by the doctors, the patients became more aware of the first symptoms’ suggesting a particular disease and the medical procedures that apply as a standard. Estimating amount of reliable factual medical information in a document is carried out by parametrizing space of digital documents and dividing it into subsequent layers that represent distribution of the system responses computed as random variables to a query about medical information. Analyzed are the following attributes: dynamism of decrease of query words numbers in the documents, precision, recall in the metric space layers, their mutual correlation and specifically the amount of reliable medical information in the documents. Sensitivity of estimators is explored in order to determine the final decision about further browsing digital documents of the metric space for more medical information that satisfies the user’s need. For identification of the true positive information in the space layer and then, in each document of this layer, matching of medical terminology with the document contents, is processed following binary Boolean search space model.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125930806","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8847104
A. Venditti, F. Fasano, M. Risi, G. Tortora
Blended learning is widely adopted by education agencies and organizations, as it is a flexible model in which face-to-face classroom practices are combined with computer-mediated activities. To overcome the limits of the loss of interaction between teacher and students and among students in distance learning, researchers proposed several solutions, conducting experiments in several teaching areas. Our interest is aimed at studying blended learning with a specific focus on those courses involving problem solving activities, through collaboration among students.Modern Learning Management Systems (LMS) allow to define virtual classrooms and offer various functionalities to support the class. At the same time, they are not designed to fully support all type of activities. Thus, they provide the possibility of integrating other more useful systems for more specific activities. A standard LMS has to be integrated using specific tools when problem solving activities are planned, to ensure effective collaboration among students. In this regard, there is no convergence towards a specific tool that can be used to carry out problem solving activities in collaboration.This paper aims to propose a minimal set of requirements for interaction mechanisms to support problem solving activities in a collaborative environment, in order to obtain better quality artifacts. We also report the results of a three-month experimental course (12 weeks) entitled ”Project Management: a look ahead”, based on blended learning and problem solving activities. The minimal set of requirements for interaction mechanisms was implemented using GitHub, that is not a teaching software, but it is a global software development tool which has powerful communication mechanisms. The results show that the aid of the proposed minimal set of requirements for interaction mechanisms significantly improves the quality of artifacts when problem solving activities are carried out.
{"title":"The importance of interaction mechanisms in blended learning courses involving problem solving e-tivities","authors":"A. Venditti, F. Fasano, M. Risi, G. Tortora","doi":"10.1109/ICDIM.2018.8847104","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8847104","url":null,"abstract":"Blended learning is widely adopted by education agencies and organizations, as it is a flexible model in which face-to-face classroom practices are combined with computer-mediated activities. To overcome the limits of the loss of interaction between teacher and students and among students in distance learning, researchers proposed several solutions, conducting experiments in several teaching areas. Our interest is aimed at studying blended learning with a specific focus on those courses involving problem solving activities, through collaboration among students.Modern Learning Management Systems (LMS) allow to define virtual classrooms and offer various functionalities to support the class. At the same time, they are not designed to fully support all type of activities. Thus, they provide the possibility of integrating other more useful systems for more specific activities. A standard LMS has to be integrated using specific tools when problem solving activities are planned, to ensure effective collaboration among students. In this regard, there is no convergence towards a specific tool that can be used to carry out problem solving activities in collaboration.This paper aims to propose a minimal set of requirements for interaction mechanisms to support problem solving activities in a collaborative environment, in order to obtain better quality artifacts. We also report the results of a three-month experimental course (12 weeks) entitled ”Project Management: a look ahead”, based on blended learning and problem solving activities. The minimal set of requirements for interaction mechanisms was implemented using GitHub, that is not a teaching software, but it is a global software development tool which has powerful communication mechanisms. The results show that the aid of the proposed minimal set of requirements for interaction mechanisms significantly improves the quality of artifacts when problem solving activities are carried out.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131655982","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 : 2018-09-01DOI: 10.1109/ICDIM.2018.8847063
A. E. Bouchti, Younes Tribis, Tarik Nahhal, C. Okar
There has been enormous attention in quantum algorithms for reinforcing machine learning (ML) algorithms. In the current paper, we present quantum neural networks (QNNs) and a method of training which is well in quantum system and is improved with momentum accession and parameter self adaptive algorithm, and we build a new financial risk forecasting model. We apply this model to the empirical research on the financial risk forecasting of some Moroccan companies. Then we will compare the findings with the standard artificial neural network (ANNs).
{"title":"Forecasting Financial Risk using Quantum Neural Networks","authors":"A. E. Bouchti, Younes Tribis, Tarik Nahhal, C. Okar","doi":"10.1109/ICDIM.2018.8847063","DOIUrl":"https://doi.org/10.1109/ICDIM.2018.8847063","url":null,"abstract":"There has been enormous attention in quantum algorithms for reinforcing machine learning (ML) algorithms. In the current paper, we present quantum neural networks (QNNs) and a method of training which is well in quantum system and is improved with momentum accession and parameter self adaptive algorithm, and we build a new financial risk forecasting model. We apply this model to the empirical research on the financial risk forecasting of some Moroccan companies. Then we will compare the findings with the standard artificial neural network (ANNs).","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121242826","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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