Nila Khrushch, Dymytrii Grytsyshen, Tetiana Baranovska, Iryna Hrabchuk, Oleksandr Shevchuk
This research aims to establish an optimized information foundation to bolster the effectiveness of financial intelligence management within the system of economic security. The chief scientific objective is to introduce an information algorithm, specifically designed for the management of financial intelligence, to fortify the economic security framework. The focal point of the research is the information support system pertaining to financial intelligence management. The research methodology is anchored in the application of contemporary information modeling methods, supplemented by functional algorithmization of processes. A modern graphic method is employed to enhance comprehensibility and accessibility. As an outcome of the study, a model of an information algorithm is presented, tailored to manage financial intelligence within the economic security system. However, the study acknowledges its limitations and does not incorporate all the elements of economic security assurance. Future research is recommended to delve into the specifics of information security within the financial intelligence management system. A distinct advantage of the proposed information algorithm lies in its graphic representation, enhancing the accessibility of the financial intelligence management system. The research scope is regional, indicating a limitation in the study. Future work should aim to expand the geographic applicability of these findings, enhancing the generalizability and relevance of the study.
{"title":"An Information Algorithm: Advancing Financial Intelligence Management for Economic Security","authors":"Nila Khrushch, Dymytrii Grytsyshen, Tetiana Baranovska, Iryna Hrabchuk, Oleksandr Shevchuk","doi":"10.18280/isi.280527","DOIUrl":"https://doi.org/10.18280/isi.280527","url":null,"abstract":"This research aims to establish an optimized information foundation to bolster the effectiveness of financial intelligence management within the system of economic security. The chief scientific objective is to introduce an information algorithm, specifically designed for the management of financial intelligence, to fortify the economic security framework. The focal point of the research is the information support system pertaining to financial intelligence management. The research methodology is anchored in the application of contemporary information modeling methods, supplemented by functional algorithmization of processes. A modern graphic method is employed to enhance comprehensibility and accessibility. As an outcome of the study, a model of an information algorithm is presented, tailored to manage financial intelligence within the economic security system. However, the study acknowledges its limitations and does not incorporate all the elements of economic security assurance. Future research is recommended to delve into the specifics of information security within the financial intelligence management system. A distinct advantage of the proposed information algorithm lies in its graphic representation, enhancing the accessibility of the financial intelligence management system. The research scope is regional, indicating a limitation in the study. Future work should aim to expand the geographic applicability of these findings, enhancing the generalizability and relevance of the study.","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"40 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976297","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}
Alex Alfredo Huaman Llanos, Lenin Quiñones Huatangari, Jeimis Royler Yalta Meza, Alexander Huaman Monteza
{"title":"Leveraging Text Mining for Analyzing Students' Preferences in Computer Science and Language Courses","authors":"Alex Alfredo Huaman Llanos, Lenin Quiñones Huatangari, Jeimis Royler Yalta Meza, Alexander Huaman Monteza","doi":"10.18280/isi.280515","DOIUrl":"https://doi.org/10.18280/isi.280515","url":null,"abstract":"","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976338","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}
Paul Menounga Mbilong, Zineb Aarab, Fatima-Zahra Belouadha, Mohammed Issam Kabbaj
ABSTRACT
{"title":"Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach","authors":"Paul Menounga Mbilong, Zineb Aarab, Fatima-Zahra Belouadha, Mohammed Issam Kabbaj","doi":"10.18280/isi.280525","DOIUrl":"https://doi.org/10.18280/isi.280525","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977609","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}
Mohammad Tanvir Parvez, Abdulaziz Mohmmad Alsuhibani, Ahmad Hussein Alamri
ABSTRACT
{"title":"Educational and Cybersecurity Applications of an Arabic CAPTCHA Gamification System","authors":"Mohammad Tanvir Parvez, Abdulaziz Mohmmad Alsuhibani, Ahmad Hussein Alamri","doi":"10.18280/isi.280516","DOIUrl":"https://doi.org/10.18280/isi.280516","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"579 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976345","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}
Fabio Sergio Bruschetti, Javier Guevara, María Claudia Abeledo, Daniel Alberto Priano
ABSTRACT
{"title":"An Empirical Evaluation of Automated Configuration Tools for Software-Defined Networking: A Usability and Performance Perspective","authors":"Fabio Sergio Bruschetti, Javier Guevara, María Claudia Abeledo, Daniel Alberto Priano","doi":"10.18280/isi.280502","DOIUrl":"https://doi.org/10.18280/isi.280502","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976500","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}
{"title":"Enhanced Intrusion Detection in Software-Defined Networks through Federated Learning and Deep Learning","authors":"Asraa A. Abd Al-Ameer, Wesam Sameer Bhaya","doi":"10.18280/isi.280509","DOIUrl":"https://doi.org/10.18280/isi.280509","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976501","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}
Zainab Ali Abd Alhuseen, Fanar Ali Joda, Mohammed Abdullah Naser
ABSTRACT
{"title":"Abnormal Behavior Detection in Gait Analysis Using Convolutional Neural Networks","authors":"Zainab Ali Abd Alhuseen, Fanar Ali Joda, Mohammed Abdullah Naser","doi":"10.18280/isi.280504","DOIUrl":"https://doi.org/10.18280/isi.280504","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"586 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976660","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}
The human face serves as a potent biological medium for expressing emotions, and the capability to interpret these expressions has been fundamental to human interaction since time immemorial. Consequently, the extraction of emotions from facial expressions in images, using machine learning, presents an intriguing yet challenging avenue. Over the past few years, advancements in artificial intelligence have significantly contributed to the field, replicating aspects of human intelligence. This paper proposes a Facial Emotion Recognition Convolutional Neural Network (FERCNN) model, addressing the limitations in accurately processing raw input images, as evidenced in the literature. A notable improvement in performance is observed when the input image is injected with noise prior to training and validation. Gaussian, Poisson, Speckle, and Salt & Pepper noise types are utilized in this noise injection process. The proposed model exhibits superior results compared to well-established CNN architectures, including VGG16, VGG19, Xception, and Resnet50. Not only does the proposed model demonstrate greater performance, but it also reduces training costs compared to models trained without noise injection at the input level. The FER2013 and JAFFE datasets, comprising seven different emotions (happy, angry, neutral, fear, disgust, sad, and surprise) and totaling 39,387 images, are used for training and testing. All experimental procedures are conducted via the Kaggle cloud infrastructure. When Gaussian, Poisson, and Speckle noise are introduced at the input level, the suggested CNN model yields evaluation accuracies of 92.17%, 95.07%, and 92.41%, respectively. In contrast, the highest accuracies achieved by existing models such as VGG16, VGG19, and Resnet 50 are 45.97%, 63.97%, and 54.52%, respectively.
{"title":"Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis","authors":"Kallam Anji Reddy, Thirupathi Regula, Karramareddy Sharmila, P.V.V.S. Srinivas, Syed Ziaur Rahman","doi":"10.18280/isi.280505","DOIUrl":"https://doi.org/10.18280/isi.280505","url":null,"abstract":"The human face serves as a potent biological medium for expressing emotions, and the capability to interpret these expressions has been fundamental to human interaction since time immemorial. Consequently, the extraction of emotions from facial expressions in images, using machine learning, presents an intriguing yet challenging avenue. Over the past few years, advancements in artificial intelligence have significantly contributed to the field, replicating aspects of human intelligence. This paper proposes a Facial Emotion Recognition Convolutional Neural Network (FERCNN) model, addressing the limitations in accurately processing raw input images, as evidenced in the literature. A notable improvement in performance is observed when the input image is injected with noise prior to training and validation. Gaussian, Poisson, Speckle, and Salt & Pepper noise types are utilized in this noise injection process. The proposed model exhibits superior results compared to well-established CNN architectures, including VGG16, VGG19, Xception, and Resnet50. Not only does the proposed model demonstrate greater performance, but it also reduces training costs compared to models trained without noise injection at the input level. The FER2013 and JAFFE datasets, comprising seven different emotions (happy, angry, neutral, fear, disgust, sad, and surprise) and totaling 39,387 images, are used for training and testing. All experimental procedures are conducted via the Kaggle cloud infrastructure. When Gaussian, Poisson, and Speckle noise are introduced at the input level, the suggested CNN model yields evaluation accuracies of 92.17%, 95.07%, and 92.41%, respectively. In contrast, the highest accuracies achieved by existing models such as VGG16, VGG19, and Resnet 50 are 45.97%, 63.97%, and 54.52%, respectively.","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"98 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977229","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}
Albertus Dwiyoga Widiantoro, Mustafid Mustafid, Ridwan Sanjaya
ABSTRACT
{"title":"Leveraging Latent Dirichlet Allocation for Feature Extraction in User Comments: Enhancements to User-Centered Design in Indonesian Financial Technology","authors":"Albertus Dwiyoga Widiantoro, Mustafid Mustafid, Ridwan Sanjaya","doi":"10.18280/isi.280530","DOIUrl":"https://doi.org/10.18280/isi.280530","url":null,"abstract":"ABSTRACT","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"154 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135977094","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：