Informatica最新文献_第5页

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

4区计算机科学

Informatica Pub Date : 2023-10-24 DOI: 10.31449/inf.v47i9.4566

Ali Abdulmunim Al-Kharaz

引用次数: 0

Retrieval of Interactive requirements for Data Intensive Applications using Random Forest Classifier 基于随机森林分类器的数据密集型应用交互需求检索

4区计算机科学

Informatica Pub Date : 2023-10-24 DOI: 10.31449/inf.v47i9.3772

Renita Raymond, Margret Anouncia Savarimuthu

引用次数: 0

Multi-Density Datasets Clustering Using K-Nearest Neighbors and Chebyshev’s Inequality 基于k近邻和Chebyshev不等式的多密度数据集聚类

4区计算机科学

Informatica Pub Date : 2023-10-06 DOI: 10.31449/inf.v47i8.4719

Amira Bouchemal, Mohamed Tahar Kimour

引用次数: 0

A Digital Evidences Preservation Framework for a Logic Based Smart Contract 基于逻辑的智能合约数字证据保存框架

4区计算机科学

Informatica Pub Date : 2023-10-06 DOI: 10.31449/inf.v47i8.4132

Walaa Alomari, Khair Eddin Sabri, Nadim Obeid

引用次数: 0

Research on financial risk prediction and prevention for small and medium-sized enterprises - based on a neural network 基于神经网络的中小企业财务风险预测与防范研究

4区计算机科学

Informatica Pub Date : 2023-10-03 DOI: 10.31449/inf.v47i8.4884

Xiaohui Wang

引用次数: 0

Research on Chord Generation in Automated Music Composition Using Deep Learning Algorithms 基于深度学习算法的自动作曲和弦生成研究

4区计算机科学

Informatica Pub Date : 2023-09-28 DOI: 10.31449/inf.v47i8.4885

Ming Zhu

{"title":"Research on Chord Generation in Automated Music Composition Using Deep Learning Algorithms","authors":"Ming Zhu","doi":"10.31449/inf.v47i8.4885","DOIUrl":"https://doi.org/10.31449/inf.v47i8.4885","url":null,"abstract":"With the development of technology, automated music composition has received widespread attention in music creation. This article mainly focuses on the generation of chords in automated music composition. First, relevant music knowledge was briefly introduced, and then the composition of the Transformer model was explained. A two-layer bidirectional Transformer method was designed to generate chords for the main melody and chorus separately, followed by the establishment of chord coloring and sound production models. Ten music professionals and 40 ordinary college students compared the coherence, pleasantness, and innovation of the chords generated by Hidden Markov Model (HMM), Long Short-Term Memory (LSTM), and the method proposed in this paper. The results showed that the chord generated by the method proposed in this paper achieved higher scores in the evaluation. Overall, the scores given by the music professionals and ordinary college students were 3.64 and 3.91, respectively, which were higher than those of the HMM and LSTM methods. The experimental results prove the superiority of the chord generation method proposed in this paper. The method can be applied to automated music composition.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135388624","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