Ingenierie des Systemes d''Information最新文献

An Information Algorithm: Advancing Financial Intelligence Management for Economic Security 一种信息算法:为经济安全推进财务智能管理

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280527

Nila Khrushch, Dymytrii Grytsyshen, Tetiana Baranovska, Iryna Hrabchuk, Oleksandr Shevchuk

{"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}

引用次数: 0

Leveraging Text Mining for Analyzing Students' Preferences in Computer Science and Language Courses 利用文本挖掘分析学生在计算机科学和语言课程中的偏好

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280515

Alex Alfredo Huaman Llanos, Lenin Quiñones Huatangari, Jeimis Royler Yalta Meza, Alexander Huaman Monteza

引用次数: 0

Enhancing Fault Detection in CNC Machinery: A Deep Learning and Genetic Algorithm Approach 增强数控机械故障检测:深度学习和遗传算法方法

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280525

Paul Menounga Mbilong, Zineb Aarab, Fatima-Zahra Belouadha, Mohammed Issam Kabbaj

引用次数: 0

Enhanced Classification of Diabetic Retinopathy via Vessel Segmentation: A Deep Ensemble Learning Approach 通过血管分割增强糖尿病视网膜病变的分类:一种深度集成学习方法

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280526

Sanjay Tanaji Sanamdikar, Mayura Vishal Shelke, Jyoti Prashant Rothe

引用次数: 0

Educational and Cybersecurity Applications of an Arabic CAPTCHA Gamification System 阿拉伯语CAPTCHA游戏化系统的教育和网络安全应用

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280516

Mohammad Tanvir Parvez, Abdulaziz Mohmmad Alsuhibani, Ahmad Hussein Alamri

引用次数: 0

An Empirical Evaluation of Automated Configuration Tools for Software-Defined Networking: A Usability and Performance Perspective 软件定义网络的自动化配置工具的实证评估:可用性和性能的视角

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280502

Fabio Sergio Bruschetti, Javier Guevara, María Claudia Abeledo, Daniel Alberto Priano

引用次数: 0

Enhanced Intrusion Detection in Software-Defined Networks through Federated Learning and Deep Learning 通过联邦学习和深度学习增强软件定义网络中的入侵检测

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280509

Asraa A. Abd Al-Ameer, Wesam Sameer Bhaya

引用次数: 0

Abnormal Behavior Detection in Gait Analysis Using Convolutional Neural Networks 卷积神经网络在步态分析中的异常行为检测

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280504

Zainab Ali Abd Alhuseen, Fanar Ali Joda, Mohammed Abdullah Naser

引用次数: 0

Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis 噪声注入FERCNN模型增强面部情绪分类性能的比较分析

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280505

Kallam Anji Reddy, Thirupathi Regula, Karramareddy Sharmila, P.V.V.S. Srinivas, Syed Ziaur Rahman

{"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}

引用次数: 0

Enhancing Anomaly-Based Intrusion Detection Systems: A Hybrid Approach Integrating Feature Selection and Bayesian Hyperparameter Optimization 增强基于异常的入侵检测系统:融合特征选择和贝叶斯超参数优化的混合方法

Ingenierie des Systemes d''Information Pub Date : 2023-10-31 DOI: 10.18280/isi.280506

Naoual Berbiche, Jamila El Alami

{"title":"Enhancing Anomaly-Based Intrusion Detection Systems: A Hybrid Approach Integrating Feature Selection and Bayesian Hyperparameter Optimization","authors":"Naoual Berbiche, Jamila El Alami","doi":"10.18280/isi.280506","DOIUrl":"https://doi.org/10.18280/isi.280506","url":null,"abstract":"In the dynamically evolving landscape of cybersecurity, safeguarding IT infrastructures has emerged as an imperative to thwart the escalation of cyber-attacks. Anomaly-based Intrusion Detection Systems (IDS) play a pivotal role in identifying aberrant behaviours that elude conventional detection mechanisms. Nonetheless, these systems are not without their shortcomings, manifesting as elevated false alarm rates and a diminished efficacy in detecting sophisticated attacks. In response to these challenges, a hybrid approach, entailing Machine Learning (ML) techniques, was employed to augment the performance of anomaly-based IDS in terms of detection accuracy, False Positive (FP) Rate, and detection time. The approach encompassed a two-fold optimization strategy: initial feature selection predicated on feature importance derived from the XGBoost classifier, followed by Bayesian optimization (BO) for hyperparameter tuning. The optimization was conducted with respect to two objective functions, namely the ROC-AUC score and the Average Precision score, each serving to identify the optimal hyperparameters for their respective maximization. Classifiers, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Stochastic Gradient Descent (SGD), were subjected to training under configurations encompassing both the hyperparameters resultant from BO and the default hyperparameters, the latter serving as reference models. Evaluation, conducted through a multifaceted metric analysis, substantiated the superiority of the optimized models over their reference counterparts, with the optimized XGBoost models demonstrating the most commendable performance. This paradigm offers a promising avenue for enhancing detection precision and mitigating false alarms, thereby fortifying the security of computer","PeriodicalId":38604,"journal":{"name":"Ingenierie des Systemes d''Information","volume":"3 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":"135978222","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}

引用次数: 0