Informatica最新文献_第5页

Circular Intuitionistic Fuzzy ELECTRE III Model for Group Decision Analysis 群体决策分析的循环直觉模糊ELECTRE III模型

IF 2.9 4区计算机科学

Informatica Pub Date : 2023-11-17 DOI: 10.15388/23-infor536

Binyamin Yusoff, Dian Pratama, Adem Kilicman, Lazim Abdullah

{"title":"Circular Intuitionistic Fuzzy ELECTRE III Model for Group Decision Analysis","authors":"Binyamin Yusoff, Dian Pratama, Adem Kilicman, Lazim Abdullah","doi":"10.15388/23-infor536","DOIUrl":"https://doi.org/10.15388/23-infor536","url":null,"abstract":"ELECTRE III is a well-established outranking relation model used to address the ranking of alternatives in multi-criteria and multi-actor decision-making problems. It has been extensively studied across various scientific fields. Due to the complexity of decision-making under uncertainty, some higher-order fuzzy sets have been proposed to effectively model this issue. Circular Intuitionistic Fuzzy Set (CIFS) is one such set recently introduced to handle uncertain IF values. In CIFS, each element of the set is characterized by a circular area with a radius, r and membership/non-membership degrees as the centre. This paper introduces CIF-ELECTRE III, an extension of ELECTRE III within the CIFS framework, for group decision analysis. To achieve this, we define extensions for the group decision matrix and group weighting vector based on CIFS conditions, particularly focusing on optimistic and pessimistic attitudes. These attitudinal characters of the group of actors are constructed using conditional rules to ensure that each element of the set falls within the circular area. Parameterized by $alpha in [0,1]$ for the net score degree, we conduct an extensive analysis of group decision-making between optimistic and pessimistic attitudes. To illustrate the applicability of the proposed model, we provide a numerical example of the stock-picking process. Additionally, we conduct a comparative analysis with existing sets and perform sensitivity analyses to validate the results of the proposed model.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"246 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543107","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}

引用次数: 0

PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition PrSChain:基于区块链的数据服务组合隐私保护方法

4区计算机科学

Informatica Pub Date : 2023-11-14 DOI: 10.31449/inf.v47i9.5081

Khemaissia Rofaida, Makhlouf Derdour, Mohamed Amine Ferrag, Mohammed Mounir Bouhamed

{"title":"PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition","authors":"Khemaissia Rofaida, Makhlouf Derdour, Mohamed Amine Ferrag, Mohammed Mounir Bouhamed","doi":"10.31449/inf.v47i9.5081","DOIUrl":"https://doi.org/10.31449/inf.v47i9.5081","url":null,"abstract":"The main goal of a Data Service Composition is combining multiple data services to provide for a user’s query a new service which uses data from multiple service providers that are incorporated in the composition. In this situation, the data privacy and especially of the service providers can be breached when their critical data can be seen by another party. Therefore, keeping the data privacy during the composition process is crucial by every work in the context of the service composition. Recent approaches rely on a central mediator that can be trusted or not to ensuring the privacy of the service providers during the query execution. The most recent approaches found problems in case of untrusted mediator where they enforce restrictions like k-protection that can affect the efficiency of their works. Therefore, we propose PrSChain which preserves the privacy of all service providers during service composition and execution using BlockChain technology. We used a permissioned BlockChain that acts as trusted mediator where it enables users to access to the BC if a valid certificate is given. We use Hyperledger Fabric to implement our solution where it stores sensitive data about the composition plan. In addition, the intermediate query results are saved in IPFS that acts as offchain storage. As a proof of concept, we have tested PrSChain on a real-world medical dataset to show its feasibility and efficiency for maintaining privacy in a secure and trusted manner.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"28 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993150","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}

引用次数: 0

Detecting Breast Cancer in X-RAY images using image segmentation algorithm and neural networks 利用图像分割算法和神经网络在x射线图像中检测乳腺癌

4区计算机科学

Informatica Pub Date : 2023-11-08 DOI: 10.31449/inf.v47i9.4995

Rasha Talib Gdeeb

{"title":"Detecting Breast Cancer in X-RAY images using image segmentation algorithm and neural networks","authors":"Rasha Talib Gdeeb","doi":"10.31449/inf.v47i9.4995","DOIUrl":"https://doi.org/10.31449/inf.v47i9.4995","url":null,"abstract":"Breast cancer becomes is a nightmare threating woman all over the world, so, all the studies are trying for early detection of it to increase healing of it, it can save 30 percent from infected women which is a big percentage. Dangerous of breast cancer comes from the fact that all the women do not know about it until they have a mammogram image for the breast. It can be detected personally in late stages. That means it is important to make a medical examination periodically to investigate the presence of any cancerous lumps in breast tissue or underarm which can be an indicator for the existence of the tumour. Mammogram rays are an X-RAY applied on the breast which can used to find any problems in the breast like tumor blocks in breast, pain, secretions from nipples. Mammogram rays can detect breast cancer early and decrease the death cases. mammogram imaging starts in 40 age and must done every 3 years to assure the not infection of it. In cases of Genetic disease history, it is important to take the mammogram imaging before 40 age in the state of early tumor detection so it increases the recovery in early stages. This work is a study to create a method to estimate the breast cancer situation in X-RAY images to select an automatic medical solution which passes in three stages, primary aiding, chemical aiding, and eradication.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"24 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390782","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}

引用次数: 0

An effective hyperspectral palmprint identification system based on deep learning and band selection approach 基于深度学习和波段选择方法的高效高光谱掌纹识别系统

4区计算机科学

Informatica Pub Date : 2023-11-08 DOI: 10.31449/inf.v46i9.4675

Maarouf Korichi, Djamel Samai, Azeddine Benlamoudi, Abdellah Meraoumia, Khaled Bensid

{"title":"An effective hyperspectral palmprint identification system based on deep learning and band selection approach","authors":"Maarouf Korichi, Djamel Samai, Azeddine Benlamoudi, Abdellah Meraoumia, Khaled Bensid","doi":"10.31449/inf.v46i9.4675","DOIUrl":"https://doi.org/10.31449/inf.v46i9.4675","url":null,"abstract":"Over the past two decades, there has been an explosion of biometric technologies because anything that characterizes a person provides a source of information. The palmprint modality is a biometric characteristic of great interest to researchers, and its traits can be found in a variety of representations, including grayscale, color, and multi/hyperspectral representations. The most difficult challenge in developing a hyperspectral palmprint-based recognition system is determining how to use all the information available in these spectral bands. In this paper, we propose a hyperspectral palmprint identification system. In the first stage, an Optimal Clustering Framework (OCF) is proposed to extract the most representative bands. Then, in order to determine the best method to describe palmprint features, two types of feature extraction methods (handcrafted and deep learning approaches) were used. After setting the number of selected bands to 4, we performed our set of experiments using the Hong Kong Polytechnic University (Poly U), which consists of 69 spectral bands. The results indicated that the proposed system offers the best performance, which qualifies it to be intended for usage in high-security situations.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"51 s179","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341548","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}

引用次数: 0

Optimizing Sequential Forward Selection on Classification using Genetic Algorithm 基于遗传算法的分类顺序正向选择优化

4区计算机科学

Informatica Pub Date : 2023-11-08 DOI: 10.31449/inf.v46i9.4964

Knitchepon Chotchantarakun

{"title":"Optimizing Sequential Forward Selection on Classification using Genetic Algorithm","authors":"Knitchepon Chotchantarakun","doi":"10.31449/inf.v46i9.4964","DOIUrl":"https://doi.org/10.31449/inf.v46i9.4964","url":null,"abstract":"Regarding the digital transformation of modern technologies, the amount of data increases significantly resulting in novel knowledge discovery techniques in Data Analytic and Data Mining. These data usually consist of noises or non-informative features which affect the analysis results. The features-eliminating approaches have been studied extensively in the past few decades name feature selection. It is a significant preprocessing step of the mining process, which selects only the informative features from the original feature set. These selected features improve the learning model efficiency. This study proposes a forward sequential feature selection method called Forward Selection with Genetic Algorithm (FS-GA). FS-GA consists of three major steps. First, it creates the preliminarily selected subsets. Second, it provides an improvement on the previous subsets. Third, it optimizes the selected subset using the genetic algorithm. Hence, it maximizes the classification accuracy during the feature addition. We performed experiments based on ten standard UCI datasets using three popular classification models including the Decision Tree, Naive Bayes, and K-Nearest Neighbour classifiers. The results are compared with the state-of-the-art methods. FS-GA has shown the best results against the other sequential forward selection methods for all the tested datasets with O(n 2 ) time complexity.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"51 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341549","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}

引用次数: 0

Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments 暗网环境中欺诈检测和取证的自主人工智能系统

4区计算机科学

Informatica Pub Date : 2023-10-26 DOI: 10.31449/inf.v46i9.4538

Romil RAWAT, Olukayode Oki, Rajesh Kumar Chakrawarti, Temitope Samson Adekunle, José Luis Arias Gonzáles, Sunday Adeola Ajagbe

引用次数: 0

A Consolidated Tree Structure Combining Multiple Regression Trees With Varying Depths, Resulting in an Efficient Ensemble Model 结合不同深度的多元回归树的整合树结构，形成高效的集成模型

4区计算机科学

Informatica Pub Date : 2023-10-26 DOI: 10.31449/inf.v47i9.3844

Elmira Ashoor Mahani, Koorush Ziarati

引用次数: 0

Optimization of Brain Cancer Images with Some Noise Models 基于噪声模型的脑癌图像优化