Pub Date : 2020-07-31DOI: 10.5121/ijaia.2020.11408
Waheeda Almayyan
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.
{"title":"Analysis of Roadway Fatal Accidents using Ensemble-based Meta-Classifiers","authors":"Waheeda Almayyan","doi":"10.5121/ijaia.2020.11408","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11408","url":null,"abstract":"In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"11 1","pages":"101-116"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41660092","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 : 2020-07-31DOI: 10.5121/ijaia.2020.11405
Amir Farzad, T. Gulliver
Imbalanced data is a significant challenge in classification with machine learning algorithms. This is particularly important with log message data as negative logs are sparse so this data is typically imbalanced. In this paper, a model to generate text log messages is proposed which employs a SeqGAN network. An Autoencoder is used for feature extraction and anomaly detection is done using a GRU network. The proposed model is evaluated with three imbalanced log data sets, namely BGL, OpenStack, and Thunderbird. Results are presented which show that appropriate oversampling and data balancing improves anomaly detection accuracy.
{"title":"Log Message Anomaly Detection with Oversampling","authors":"Amir Farzad, T. Gulliver","doi":"10.5121/ijaia.2020.11405","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11405","url":null,"abstract":"Imbalanced data is a significant challenge in classification with machine learning algorithms. This is particularly important with log message data as negative logs are sparse so this data is typically imbalanced. In this paper, a model to generate text log messages is proposed which employs a SeqGAN network. An Autoencoder is used for feature extraction and anomaly detection is done using a GRU network. The proposed model is evaluated with three imbalanced log data sets, namely BGL, OpenStack, and Thunderbird. Results are presented which show that appropriate oversampling and data balancing improves anomaly detection accuracy.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2020.11405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46992845","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 : 2020-07-31DOI: 10.5121/ijaia.2020.11407
Damir Dobric, Andreas Pech, B. Ghita, T. Wennekers
The Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is a theory and machine learning technology that aims to capture cortical algorithm of the neocortex. Inspired by the biological functioning of the neocortex, it provides a theoretical framework, which helps to better understand how the cortical algorithm inside of the brain might work. It organizes populations of neurons in column-like units, crossing several layers such that the units are connected into structures called regions (areas). Areas and columns are hierarchically organized and can further be connected into more complex networks, which implement higher cognitive capabilities like invariant representations. Columns inside of layers are specialized on learning of spatial patterns and sequences. This work targets specifically spatial pattern learning algorithm called Spatial Pooler. A complex topology and high number of neurons used in this algorithm, require more computing power than even a single machine with multiple cores or a GPUs could provide. This work aims to improve the HTM CLA Spatial Pooler by enabling it to run in the distributed environment on multiple physical machines by using the Actor Programming Model. The proposed model is based on a mathematical theory and computation model, which targets massive concurrency. Using this model drives different reasoning about concurrent execution and enables flexible distribution of parallel cortical computation logic across multiple physical nodes. This work is the first one about the parallel HTM Spatial Pooler on multiple physical nodes with named computational model. With the increasing popularity of cloud computing and server less architectures, it is the first step towards proposing interconnected independent HTM CLA units in an elastic cognitive network. Thereby it can provide an alternative to deep neuronal networks, with theoretically unlimited scale in a distributed cloud environment.
{"title":"Scaling the HTM Spatial Pooler","authors":"Damir Dobric, Andreas Pech, B. Ghita, T. Wennekers","doi":"10.5121/ijaia.2020.11407","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11407","url":null,"abstract":"The Hierarchical Temporal Memory Cortical Learning Algorithm (HTM CLA) is a theory and machine learning technology that aims to capture cortical algorithm of the neocortex. Inspired by the biological functioning of the neocortex, it provides a theoretical framework, which helps to better understand how the cortical algorithm inside of the brain might work. It organizes populations of neurons in column-like units, crossing several layers such that the units are connected into structures called regions (areas). Areas and columns are hierarchically organized and can further be connected into more complex networks, which implement higher cognitive capabilities like invariant representations. Columns inside of layers are specialized on learning of spatial patterns and sequences. This work targets specifically spatial pattern learning algorithm called Spatial Pooler. A complex topology and high number of neurons used in this algorithm, require more computing power than even a single machine with multiple cores or a GPUs could provide. This work aims to improve the HTM CLA Spatial Pooler by enabling it to run in the distributed environment on multiple physical machines by using the Actor Programming Model. The proposed model is based on a mathematical theory and computation model, which targets massive concurrency. Using this model drives different reasoning about concurrent execution and enables flexible distribution of parallel cortical computation logic across multiple physical nodes. This work is the first one about the parallel HTM Spatial Pooler on multiple physical nodes with named computational model. With the increasing popularity of cloud computing and server less architectures, it is the first step towards proposing interconnected independent HTM CLA units in an elastic cognitive network. Thereby it can provide an alternative to deep neuronal networks, with theoretically unlimited scale in a distributed cloud environment.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47034744","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 : 2020-07-31DOI: 10.5121/ijaia.2020.11402
Mikel David Jedrusiak, F. Weichert
Ensuring material quality is a central objective in production and manufacturing. Non-contact nondestructive testing methods without the use of coupling media are of particular interest with regard to mechanical or biochemical properties of the material. For this purpose, air-coupled ultrasonic is a useful method for quality control. The challenge is the poor signal-to-noise ratio, which makes it difficult to apply the classical approaches. This makes it impossible to distinguish between defect structures and noise. We are developing a method for denoising air-coupled ultrasonic data by applying deep neural networks by using a geometry-analytical component that detects defect structures. During the evaluation we show that we are able to obtain the data almost free of noise, so that incorrectly classified noisy pixels are mainly located at the edges of the defect structures, which cannot be clearly delimited. It is shown that the quality of the data is significantly improved for detection processes.
{"title":"A Deep Learning Approach for Denoising Air-Coupled Ultrasonic Responds Data","authors":"Mikel David Jedrusiak, F. Weichert","doi":"10.5121/ijaia.2020.11402","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11402","url":null,"abstract":"Ensuring material quality is a central objective in production and manufacturing. Non-contact nondestructive testing methods without the use of coupling media are of particular interest with regard to mechanical or biochemical properties of the material. For this purpose, air-coupled ultrasonic is a useful method for quality control. The challenge is the poor signal-to-noise ratio, which makes it difficult to apply the classical approaches. This makes it impossible to distinguish between defect structures and noise. We are developing a method for denoising air-coupled ultrasonic data by applying deep neural networks by using a geometry-analytical component that detects defect structures. During the evaluation we show that we are able to obtain the data almost free of noise, so that incorrectly classified noisy pixels are mainly located at the edges of the defect structures, which cannot be clearly delimited. It is shown that the quality of the data is significantly improved for detection processes.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"11 1","pages":"15-28"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2020.11402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46235100","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 : 2020-07-31DOI: 10.5121/ijaia.2020.11404
Koffka Khan, E. Ramsahai
Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety, Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.
{"title":"Categorizing 2019-n-CoV Twitter Hashtag Data by Clustering","authors":"Koffka Khan, E. Ramsahai","doi":"10.5121/ijaia.2020.11404","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11404","url":null,"abstract":"Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook. Clustering enables the finding of patterns in these unstructured datasets. We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset. We compared the performance of nine clustering algorithms using this dataset. We evaluated the generalizability of these algorithms using a supervised learning model. Finally, using a selected unsupervised learning algorithm we categorized the clusters. The top five categories are Safety, Crime, Products, Countries and Health. This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46062468","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 : 2020-05-31DOI: 10.5121/ijaia.2020.11301
Shi Wenxiu, Li Nianqiang
Combining with deep learning technology, this paper proposes a method of farmland pest recognition based on target detection algorithm, which realizes the automatic recognition of farmland pest and improves the recognition accuracy. First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.
{"title":"Application of Target Detection Algorithm based on Deep Learning in Farmland Pest Recognition","authors":"Shi Wenxiu, Li Nianqiang","doi":"10.5121/ijaia.2020.11301","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11301","url":null,"abstract":"Combining with deep learning technology, this paper proposes a method of farmland pest recognition based on target detection algorithm, which realizes the automatic recognition of farmland pest and improves the recognition accuracy. First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2020.11301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42284619","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 : 2020-05-31DOI: 10.5121/ijaia.2020.11302
Suhilah Alkhalifah, Adel Aloraini
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
{"title":"Graphical Model and Clustering-Regression based Methods for Causal Interactions: Breast Cancer Case Study","authors":"Suhilah Alkhalifah, Adel Aloraini","doi":"10.5121/ijaia.2020.11302","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11302","url":null,"abstract":"The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"11 1","pages":"11-27"},"PeriodicalIF":0.0,"publicationDate":"2020-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48135526","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 : 2020-03-31DOI: 10.5121/ijaia.2020.11204
Mohammed Zakaria Moustafa, Mohammed Rizk Mohammed, H. Khater, Hager Ali Yahia
A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
{"title":"A BI-objective Model for SVM With an Interactive Procedure to Identify the Best Compromise Solution","authors":"Mohammed Zakaria Moustafa, Mohammed Rizk Mohammed, H. Khater, Hager Ali Yahia","doi":"10.5121/ijaia.2020.11204","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11204","url":null,"abstract":"A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"11 1","pages":"47"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44078465","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 : 2020-03-31DOI: 10.5121/ijaia.2020.11202
A. Massaro, G. Dipierro, A. Saponaro, A. Galiano
The proposed study deals with the design and the development of a Decision Support System (DSS) platform suitable for the global distribution system (GDS). Precisely, the prototype platform combines artificial intelligence and data mining algorithms to process data collected into a Cassandra Big Data system. In the first part of the paper platform architectures together with all the adopted frameworks including Key Performance Indicators (KPIs) definitions and risk mapping design have been discussed. In the second part data mining algorithms have been applied in order to predict main KPIs. The adopted artificial neural networks architectures are Long Short-Term Memory (LSTM), standard Recurrent Neural Network (RNN) and Gated Recurrent Units (GRU). A dataset with KPIs has been generated in order to test the algorithms. All performed algorithms show a good matching with the generated dataset, thus proving to be the correct approach to predict KPIs. The best performances in terms of Accuracy and Loss are reached by using the standard RNN. The proposed platform represents a solution to increase the Knowledge Base (KB) for a strategic marketing and advanced business intelligence operations.
{"title":"Data Mining Applied in Food Trade Network","authors":"A. Massaro, G. Dipierro, A. Saponaro, A. Galiano","doi":"10.5121/ijaia.2020.11202","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11202","url":null,"abstract":"The proposed study deals with the design and the development of a Decision Support System (DSS) platform suitable for the global distribution system (GDS). Precisely, the prototype platform combines artificial intelligence and data mining algorithms to process data collected into a Cassandra Big Data system. In the first part of the paper platform architectures together with all the adopted frameworks including Key Performance Indicators (KPIs) definitions and risk mapping design have been discussed. In the second part data mining algorithms have been applied in order to predict main KPIs. The adopted artificial neural networks architectures are Long Short-Term Memory (LSTM), standard Recurrent Neural Network (RNN) and Gated Recurrent Units (GRU). A dataset with KPIs has been generated in order to test the algorithms. All performed algorithms show a good matching with the generated dataset, thus proving to be the correct approach to predict KPIs. The best performances in terms of Accuracy and Loss are reached by using the standard RNN. The proposed platform represents a solution to increase the Knowledge Base (KB) for a strategic marketing and advanced business intelligence operations.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"11 1","pages":"15-35"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2020.11202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46890590","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 : 2020-03-31DOI: 10.5121/ijaia.2020.11203
Callistus Ireneous Nakpih, S. Santini
This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.
{"title":"Automated Discovery of Logical Fallacies in Legal Argumentation","authors":"Callistus Ireneous Nakpih, S. Santini","doi":"10.5121/ijaia.2020.11203","DOIUrl":"https://doi.org/10.5121/ijaia.2020.11203","url":null,"abstract":"This paper presents a model of an algorithmic framework and a system for the discovery of non sequitur fallacies in legal argumentation. The model functions on formalised legal text implemented in Prolog. Different parts of the formalised legal text for legal decision-making processes such as, claim of a plaintiff, the piece of law applied to the case, and the decision of judge, will be assessed by the algorithm, for detecting fallacies in an argument. We provide a mechanism designed to assess the coherence of every premise of a claim, their logic structure and legal consistency, with their corresponding piece of law at each stage of the argumentation. The modelled system checks for validity and soundness of a claim, as well as sufficiency and necessity of the premise of arguments. We assert that, dealing with the challenges of validity, soundness, sufficiency and necessity resolves fallacies in argumentation.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2020.11203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41459438","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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