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.
{"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}
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.
{"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}
Romil RAWAT, Olukayode Oki, Rajesh Kumar Chakrawarti, Temitope Samson Adekunle, José Luis Arias Gonzáles, Sunday Adeola Ajagbe
{"title":"Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments","authors":"Romil RAWAT, Olukayode Oki, Rajesh Kumar Chakrawarti, Temitope Samson Adekunle, José Luis Arias Gonzáles, Sunday Adeola Ajagbe","doi":"10.31449/inf.v46i9.4538","DOIUrl":"https://doi.org/10.31449/inf.v46i9.4538","url":null,"abstract":"","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"26 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909387","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}
{"title":"A Consolidated Tree Structure Combining Multiple Regression Trees With Varying Depths, Resulting in an Efficient Ensemble Model","authors":"Elmira Ashoor Mahani, Koorush Ziarati","doi":"10.31449/inf.v47i9.3844","DOIUrl":"https://doi.org/10.31449/inf.v47i9.3844","url":null,"abstract":"","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"160 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908942","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}
{"title":"Optimization of Brain Cancer Images with Some Noise Models","authors":"Ali Abdulmunim Al-Kharaz","doi":"10.31449/inf.v47i9.4566","DOIUrl":"https://doi.org/10.31449/inf.v47i9.4566","url":null,"abstract":"","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"10 S1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135321824","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}
{"title":"Retrieval of Interactive requirements for Data Intensive Applications using Random Forest Classifier","authors":"Renita Raymond, Margret Anouncia Savarimuthu","doi":"10.31449/inf.v47i9.3772","DOIUrl":"https://doi.org/10.31449/inf.v47i9.3772","url":null,"abstract":"","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"99 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135322167","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}
Density-based clustering techniques are widely used in data mining on various fields. DBSCAN is one of the most popular density-based clustering algorithms, characterized by its ability to discover clusters with different shapes and sizes, and to separate noise and outliers. However, two fundamental limitations are still encountered that is the required input parameter of Eps distance threshold and its inefficiency to cluster datasets with various densities. For overcoming such drawbacks, a statistical based technique is proposed in this work. Specifically, the proposed technique utilizes an appropriate k-nearest neighbor density, based on which it sorts the dataset in ascending order and, using the statistical Chebyshev’s inequality as a suitable means for handling arbitrary distributions, it automatically determines different Eps values for clusters of various densities. Experiments conducted on synthetic and real datasets have demonstrated its efficiency and accuracy. The results indicate its superiority compared with DBSCAN, DPC, and their recently proposed improvements.
{"title":"Multi-Density Datasets Clustering Using K-Nearest Neighbors and Chebyshev’s Inequality","authors":"Amira Bouchemal, Mohamed Tahar Kimour","doi":"10.31449/inf.v47i8.4719","DOIUrl":"https://doi.org/10.31449/inf.v47i8.4719","url":null,"abstract":"Density-based clustering techniques are widely used in data mining on various fields. DBSCAN is one of the most popular density-based clustering algorithms, characterized by its ability to discover clusters with different shapes and sizes, and to separate noise and outliers. However, two fundamental limitations are still encountered that is the required input parameter of Eps distance threshold and its inefficiency to cluster datasets with various densities. For overcoming such drawbacks, a statistical based technique is proposed in this work. Specifically, the proposed technique utilizes an appropriate k-nearest neighbor density, based on which it sorts the dataset in ascending order and, using the statistical Chebyshev’s inequality as a suitable means for handling arbitrary distributions, it automatically determines different Eps values for clusters of various densities. Experiments conducted on synthetic and real datasets have demonstrated its efficiency and accuracy. The results indicate its superiority compared with DBSCAN, DPC, and their recently proposed improvements.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351312","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}
Recently, smart contracts were introduced as a necessity to automatically execute specific operations within blockchain systems. The popularity and diversity of blockchain systems attracted intensive attentions from academia, industry and other sectors. Blockchain systems were implemented using different programming languages that used in defining the triggering events and their consequent actions within the smart contract. In this article, we propose a digital evidences preservation framework that supports logic-based smart contracts to manage entries associated with digital evidences. Combining logic-based approach and blockchain systems may result in ensuing contracts that have technical advantages over procedural coding. The paper shows the motivation for choosing logic-based approach to define a smart contract. We introduce the rules and structure of the proposed logic-based contract.
{"title":"A Digital Evidences Preservation Framework for a Logic Based Smart Contract","authors":"Walaa Alomari, Khair Eddin Sabri, Nadim Obeid","doi":"10.31449/inf.v47i8.4132","DOIUrl":"https://doi.org/10.31449/inf.v47i8.4132","url":null,"abstract":"Recently, smart contracts were introduced as a necessity to automatically execute specific operations within blockchain systems. The popularity and diversity of blockchain systems attracted intensive attentions from academia, industry and other sectors. Blockchain systems were implemented using different programming languages that used in defining the triggering events and their consequent actions within the smart contract. In this article, we propose a digital evidences preservation framework that supports logic-based smart contracts to manage entries associated with digital evidences. Combining logic-based approach and blockchain systems may result in ensuing contracts that have technical advantages over procedural coding. The paper shows the motivation for choosing logic-based approach to define a smart contract. We introduce the rules and structure of the proposed logic-based contract.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351258","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}
For companies, timely and accurate risk prediction plays an an essential role in sustaining business growth. In this paper, firstly, the financial risk of small and medium-sized enterprises (SMEs) was simply analyzed. Some financial indicators were selected, and then some of the indicators were eliminated by Mann-Whitney U test and Pearson test. For risk prediction, an improved sparrow search algorithm-back-propagation neural network (ISSA-BPNN) method was designed by optimizing the BPNN with the piecewise linear chaotic map (PWLCM)-improved SSA. Experiments were performed on 82 special treatment (ST) enterprises and 164 non-ST enterprises. The results showed that the BPNN had higher accuracy in risk prediction than methods such as Fisher discriminant analysis; the optimization of the ISSA for the BPNN was reliable as the accuracy and F1 value of the ISSA-BPNN method were 0.9834 and 0.9425, respectively; the prediction was wrong for only one sample out of 20 randomly selected samples. The results demonstrate the reliability and practical applicability of the ISSA-BPNN method.
{"title":"Research on financial risk prediction and prevention for small and medium-sized enterprises - based on a neural network","authors":"Xiaohui Wang","doi":"10.31449/inf.v47i8.4884","DOIUrl":"https://doi.org/10.31449/inf.v47i8.4884","url":null,"abstract":"For companies, timely and accurate risk prediction plays an an essential role in sustaining business growth. In this paper, firstly, the financial risk of small and medium-sized enterprises (SMEs) was simply analyzed. Some financial indicators were selected, and then some of the indicators were eliminated by Mann-Whitney U test and Pearson test. For risk prediction, an improved sparrow search algorithm-back-propagation neural network (ISSA-BPNN) method was designed by optimizing the BPNN with the piecewise linear chaotic map (PWLCM)-improved SSA. Experiments were performed on 82 special treatment (ST) enterprises and 164 non-ST enterprises. The results showed that the BPNN had higher accuracy in risk prediction than methods such as Fisher discriminant analysis; the optimization of the ISSA for the BPNN was reliable as the accuracy and F1 value of the ISSA-BPNN method were 0.9834 and 0.9425, respectively; the prediction was wrong for only one sample out of 20 randomly selected samples. The results demonstrate the reliability and practical applicability of the ISSA-BPNN method.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135739379","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}
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.
{"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}
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