Pub Date : 2022-11-17DOI: 10.1142/s0218213023400018
M.Y.S. Ali, M. Jabreel, A. Valls, M. Baget, M. Abdel-Nasser
{"title":"Glaucoma Detection in Retinal Fundus Images Based on Deep Transfer Learning and Fuzzy Aggregation Operators","authors":"M.Y.S. Ali, M. Jabreel, A. Valls, M. Baget, M. Abdel-Nasser","doi":"10.1142/s0218213023400018","DOIUrl":"https://doi.org/10.1142/s0218213023400018","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"1 1","pages":"2340001:1-2340001:28"},"PeriodicalIF":1.1,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73381762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-17DOI: 10.1142/s021821302340002x
H. K. Bhuyan, V. Ravi
{"title":"An Integrated Framework with Deep Learning for Segmentation and Classification of Cancer Disease","authors":"H. K. Bhuyan, V. Ravi","doi":"10.1142/s021821302340002x","DOIUrl":"https://doi.org/10.1142/s021821302340002x","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"40 1","pages":"2340002:1-2340002:29"},"PeriodicalIF":1.1,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87067662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-17DOI: 10.1142/s0218213023400043
A. Verma, P. Jain, T. Kumar
{"title":"An Effective Depression Diagnostic System Using Speech Signal Analysis Through Deep Learning Methods","authors":"A. Verma, P. Jain, T. Kumar","doi":"10.1142/s0218213023400043","DOIUrl":"https://doi.org/10.1142/s0218213023400043","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"38 1","pages":"2340004:1-2340004:17"},"PeriodicalIF":1.1,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74762268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.1142/s0218213023400134
S. Balaji, U. Rahamathunnisa
{"title":"Multimodal Biometrics Authentication in Healthcare Using Improved Convolution Deep Learning Model","authors":"S. Balaji, U. Rahamathunnisa","doi":"10.1142/s0218213023400134","DOIUrl":"https://doi.org/10.1142/s0218213023400134","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"21 1","pages":"2340013:1-2340013:18"},"PeriodicalIF":1.1,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88946289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-12DOI: 10.1142/s0218213023500124
S. Kayikci, Nazeer Unnisa, Anupam Das, S. K. R. Kanna, Mantripragada Yaswanth Bhanu Murthy, Ninu Preetha Nirmala Sreedharan, Brammya Ganesan
{"title":"Deep Learning with Game Theory Assisted Vertical Handover Optimization in a Heterogeneous Network","authors":"S. Kayikci, Nazeer Unnisa, Anupam Das, S. K. R. Kanna, Mantripragada Yaswanth Bhanu Murthy, Ninu Preetha Nirmala Sreedharan, Brammya Ganesan","doi":"10.1142/s0218213023500124","DOIUrl":"https://doi.org/10.1142/s0218213023500124","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"44 1","pages":"2350012:1-2350012:33"},"PeriodicalIF":1.1,"publicationDate":"2022-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83013145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1142/s0218213023400183
N. Jain, V. Gupta, U. Tariq, D. Hemanth
{"title":"Fast Violence Recognition in Video Surveillance by Integrating Object Detection and Conv-LSTM","authors":"N. Jain, V. Gupta, U. Tariq, D. Hemanth","doi":"10.1142/s0218213023400183","DOIUrl":"https://doi.org/10.1142/s0218213023400183","url":null,"abstract":"","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"34 1","pages":"2340018:1-2340018:23"},"PeriodicalIF":1.1,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89909228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1142/s0218213022600077
S. Doutre, Mickael Lafages, M. Lagasquie-Schiex
Computation and decision problems related to argumentation frameworks with higher-order attacks have not received a lot of attention so far. This paper is a step towards these issues. First, it provides a labelling counterpart for the structure semantics of Recursive Argumentation Frameworks (RAF). Second, it investigates the complexity of decision problems associated with RAF. This investigation shows that, for the higher expressiveness offered by these enriched systems, the complexity is the same as for classical argumentation frameworks. As a side contribution, a new semantics for RAF, the semi-stable semantics, and a new process for translating RAF into Argumentation Frameworks without higher-order attacks (AF), are introduced. Finally, new notions which are the counterparts of equivalent notions already existing for AF (among them, the Strongly Connected Components — SCC) are defined and investigated in order to involve them in the future development of algorithms for computing RAF labelling semantics.
{"title":"Towards Algorithms for Argumentation Frameworks with Higher-order Attacks","authors":"S. Doutre, Mickael Lafages, M. Lagasquie-Schiex","doi":"10.1142/s0218213022600077","DOIUrl":"https://doi.org/10.1142/s0218213022600077","url":null,"abstract":"Computation and decision problems related to argumentation frameworks with higher-order attacks have not received a lot of attention so far. This paper is a step towards these issues. First, it provides a labelling counterpart for the structure semantics of Recursive Argumentation Frameworks (RAF). Second, it investigates the complexity of decision problems associated with RAF. This investigation shows that, for the higher expressiveness offered by these enriched systems, the complexity is the same as for classical argumentation frameworks. As a side contribution, a new semantics for RAF, the semi-stable semantics, and a new process for translating RAF into Argumentation Frameworks without higher-order attacks (AF), are introduced. Finally, new notions which are the counterparts of equivalent notions already existing for AF (among them, the Strongly Connected Components — SCC) are defined and investigated in order to involve them in the future development of algorithms for computing RAF labelling semantics.","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"117 1","pages":"2260007:1-2260007:75"},"PeriodicalIF":1.1,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77677101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1142/s0218213022600041
Michel Medema, A. Lazovik
A constraint satisfaction problem (CSP) is, in its most general form, an NP-complete problem. One of the several classes of tractable problems that exist contains all the problems with a restricted structure of the constraint scopes. This paper studies community structure, a particular type of restricted structure underpinning a class of tractable SAT problems with potentially similar relevance to CSPs. Using the modularity, it explores the community structure of a wide variety of problems with both academic and industrial relevance. Its impact on the search times of several general solvers, as well as one that uses tree-decomposition, is also analysed to determine whether constraint solvers exploit this type of structure. Nearly all CSP instances have a strong community structure, and those belonging to the same class have comparable modularity values. For the general solvers, strong correlations between the community structure and the search times are not apparent. A more definite correlation exists between the modularity and the search times of the tree-decomposition, suggesting that it might, in part, be able to take advantage of the community structure. However, combined with the relatively strong correlation between the modularity and the tree-width, it could also indicate a similarity between these two measures.
{"title":"Correlating the Community Structure of Constraint Satisfaction Problems with Search Time","authors":"Michel Medema, A. Lazovik","doi":"10.1142/s0218213022600041","DOIUrl":"https://doi.org/10.1142/s0218213022600041","url":null,"abstract":"A constraint satisfaction problem (CSP) is, in its most general form, an NP-complete problem. One of the several classes of tractable problems that exist contains all the problems with a restricted structure of the constraint scopes. This paper studies community structure, a particular type of restricted structure underpinning a class of tractable SAT problems with potentially similar relevance to CSPs. Using the modularity, it explores the community structure of a wide variety of problems with both academic and industrial relevance. Its impact on the search times of several general solvers, as well as one that uses tree-decomposition, is also analysed to determine whether constraint solvers exploit this type of structure. Nearly all CSP instances have a strong community structure, and those belonging to the same class have comparable modularity values. For the general solvers, strong correlations between the community structure and the search times are not apparent. A more definite correlation exists between the modularity and the search times of the tree-decomposition, suggesting that it might, in part, be able to take advantage of the community structure. However, combined with the relatively strong correlation between the modularity and the tree-width, it could also indicate a similarity between these two measures.","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"2 1","pages":"2260004:1-2260004:28"},"PeriodicalIF":1.1,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91307529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1142/s0218213022600028
Nassim Bahri, Mohamed Anis Bach Tobji, B. B. Yaghlane
Rule-based classifiers use a collection of high-quality rules to classify new data instances. They can be categorized according to the adopted classification strategy: Classifiers based on a single rule, and classifiers based on multiple rules. Many works were proposed in this field. However, most of them do not handle imperfect data. In this study, we focus on the issue of multi-rules-based classification for evidential data, i.e., data where imperfection is modeled via the belief functions theory. In this respect, we introduce a new algorithm called PWEviRC. This latter involves a two-level pruning technique to remove redundant and noisy rules. Finally, it applies the Dempster rule of combination to fuse the selected rules and make the final decision. To evaluate the proposed method, we carried out extensive experiments on several benchmark data sets. The performance study showed interesting results in comparison to existing methods.
{"title":"WPEviRC: A Multi-rules-based Classifier for Evidential Databases Without Class Label Ambiguities","authors":"Nassim Bahri, Mohamed Anis Bach Tobji, B. B. Yaghlane","doi":"10.1142/s0218213022600028","DOIUrl":"https://doi.org/10.1142/s0218213022600028","url":null,"abstract":"Rule-based classifiers use a collection of high-quality rules to classify new data instances. They can be categorized according to the adopted classification strategy: Classifiers based on a single rule, and classifiers based on multiple rules. Many works were proposed in this field. However, most of them do not handle imperfect data. In this study, we focus on the issue of multi-rules-based classification for evidential data, i.e., data where imperfection is modeled via the belief functions theory. In this respect, we introduce a new algorithm called PWEviRC. This latter involves a two-level pruning technique to remove redundant and noisy rules. Finally, it applies the Dempster rule of combination to fuse the selected rules and make the final decision. To evaluate the proposed method, we carried out extensive experiments on several benchmark data sets. The performance study showed interesting results in comparison to existing methods.","PeriodicalId":50280,"journal":{"name":"International Journal on Artificial Intelligence Tools","volume":"50 1","pages":"2260002:1-2260002:24"},"PeriodicalIF":1.1,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80785081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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