Abstract Many people are interested in instrumental music. They may have one piece of song, but it is a challenge to seek the song because they do not have lyrics to describe for a text-based search engine. This study leverages the Approximate Nearest Neighbours to preprocess the instrumental songs and extract the characteristics of the track in the repository using the Mel frequency cepstral coefficients (MFCC) characteristic extraction. Our method digitizes the track, extracts the track characteristics, and builds the index tree with different lengths of each MFCC and dimension number of vectors. We collected songs played with various instruments for the experiments. Our result on 100 pieces of various songs in different lengths, with a sampling rate of 16000 and a length of each MFCC of 13, gives the best results, where accuracy on the Top 1 is 36 %, Top 5 is 4 %, and Top 10 is 44 %. We expect this work to provide useful tools to develop digital music e-commerce systems.
{"title":"Approximate Nearest Neighbour-based Index Tree: A Case Study for Instrumental Music Search","authors":"Nguyen Ha Thanh, Linh Dan Vo, Thien Thanh Tran","doi":"10.2478/acss-2023-0015","DOIUrl":"https://doi.org/10.2478/acss-2023-0015","url":null,"abstract":"Abstract Many people are interested in instrumental music. They may have one piece of song, but it is a challenge to seek the song because they do not have lyrics to describe for a text-based search engine. This study leverages the Approximate Nearest Neighbours to preprocess the instrumental songs and extract the characteristics of the track in the repository using the Mel frequency cepstral coefficients (MFCC) characteristic extraction. Our method digitizes the track, extracts the track characteristics, and builds the index tree with different lengths of each MFCC and dimension number of vectors. We collected songs played with various instruments for the experiments. Our result on 100 pieces of various songs in different lengths, with a sampling rate of 16000 and a length of each MFCC of 13, gives the best results, where accuracy on the Top 1 is 36 %, Top 5 is 4 %, and Top 10 is 44 %. We expect this work to provide useful tools to develop digital music e-commerce systems.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78403452","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}
Abstract Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to develop fast, robust, and intelligent chest X-ray (CXR) image classification system. The proposed method consists of two major components: feature extraction and classification. The Bag of image features algorithm creates visual vocabulary from two training data categories of chest X-ray images: Normal and COVID-19 patients’ datasets. The algorithm extracts salient features and descriptors from CXR images using the Speeded Up Robust Features (SURF) algorithm. Machine learning with the Clustering-Based Support Vector Machines (CB-SVMs) multiclass classifier is trained using SURF features to classify the CXR image categories. The careful collection of ground truth Normal and COVID-19 CXR datasets, provided by worldwide expert radiologists, has certainly influenced the performance of the proposed CB-SVMs classifier to preserve the generalization capabilities. The high classification accuracy of 99 % demonstrates the effectiveness of the proposed method, where the accuracy is assessed on an independent test sets.
{"title":"Classification of COVID-19 Chest X-Ray Images Based on Speeded Up Robust Features and Clustering-Based Support Vector Machines","authors":"M. Rajab","doi":"10.2478/acss-2023-0016","DOIUrl":"https://doi.org/10.2478/acss-2023-0016","url":null,"abstract":"Abstract Due to the worldwide deficiency of medical test kits and the significant time required by radiology experts to identify the new COVID-19, it is essential to develop fast, robust, and intelligent chest X-ray (CXR) image classification system. The proposed method consists of two major components: feature extraction and classification. The Bag of image features algorithm creates visual vocabulary from two training data categories of chest X-ray images: Normal and COVID-19 patients’ datasets. The algorithm extracts salient features and descriptors from CXR images using the Speeded Up Robust Features (SURF) algorithm. Machine learning with the Clustering-Based Support Vector Machines (CB-SVMs) multiclass classifier is trained using SURF features to classify the CXR image categories. The careful collection of ground truth Normal and COVID-19 CXR datasets, provided by worldwide expert radiologists, has certainly influenced the performance of the proposed CB-SVMs classifier to preserve the generalization capabilities. The high classification accuracy of 99 % demonstrates the effectiveness of the proposed method, where the accuracy is assessed on an independent test sets.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79090306","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}
V. Halchenko, R. Trembovetska, V. Tychkov, N. Tychkova
Abstract In order to establish the projection properties of computer uniform designs of experiments on Sobol’s sequences, an empirical comparative statistical analysis of the homogeneity of 2D projections of the best known improved designs of experiments was carried out using the novel objective indicators of discrepancies. These designs show an incomplete solution to the problem of clustering points in low-dimensional projections graphically and numerically, which requires further research for new Sobol’s sequences without the drawback mentioned above. In the article, using the example of the first 20 improved Sobol’s sequences, a methodology for creating refined designs is proposed, which is based on the unconventional use of these already found sequences. It involves the creation of the next dimensional design based on the best homogeneity and projection properties of the previous one. The selection of sequences for creating an initial design is based on the analysis of numerical indicators of the weighted symmetrized centered discrepancy for two-dimensional projections. According to the algorithm, the combination of sequences is fixed for the found variant and a complete search of the added one-dimensional sequences is performed until the best one is detected. According to the proposed methodology, as an example, a search for more perfect variants of designs for factor spaces from two to nine dimensions was carried out. New combinations of Sobol’s sequences with better projection properties than those already known are given. Their effectiveness is confirmed by statistical calculations and graphically demonstrated box plots and histograms of the projection indicators distribution of the weighted symmetrized centred discrepancy. In addition, the numerical results of calculating the volumetric indicators of discrepancies for the created designs with different number of points are given.
{"title":"Construction of Quasi-DOE on Sobol’s Sequences with Better Uniformity 2D Projections","authors":"V. Halchenko, R. Trembovetska, V. Tychkov, N. Tychkova","doi":"10.2478/acss-2023-0003","DOIUrl":"https://doi.org/10.2478/acss-2023-0003","url":null,"abstract":"Abstract In order to establish the projection properties of computer uniform designs of experiments on Sobol’s sequences, an empirical comparative statistical analysis of the homogeneity of 2D projections of the best known improved designs of experiments was carried out using the novel objective indicators of discrepancies. These designs show an incomplete solution to the problem of clustering points in low-dimensional projections graphically and numerically, which requires further research for new Sobol’s sequences without the drawback mentioned above. In the article, using the example of the first 20 improved Sobol’s sequences, a methodology for creating refined designs is proposed, which is based on the unconventional use of these already found sequences. It involves the creation of the next dimensional design based on the best homogeneity and projection properties of the previous one. The selection of sequences for creating an initial design is based on the analysis of numerical indicators of the weighted symmetrized centered discrepancy for two-dimensional projections. According to the algorithm, the combination of sequences is fixed for the found variant and a complete search of the added one-dimensional sequences is performed until the best one is detected. According to the proposed methodology, as an example, a search for more perfect variants of designs for factor spaces from two to nine dimensions was carried out. New combinations of Sobol’s sequences with better projection properties than those already known are given. Their effectiveness is confirmed by statistical calculations and graphically demonstrated box plots and histograms of the projection indicators distribution of the weighted symmetrized centred discrepancy. In addition, the numerical results of calculating the volumetric indicators of discrepancies for the created designs with different number of points are given.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86096140","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}
Nguyen Ha Thanh, Tuyet Ngoc Huynh, Nhi Mai, K. D. Le, Pham Thi-Ngoc-Diem
Abstract Besides the successful use of support software in cutting-edge medical procedures, the significance of determining a disease early signs and symptoms before its detection is a growing pressing requirement to raise the standard of medical examination and treatment. This creates favourable conditions, reduces patient inconvenience and hospital overcrowding. Before transferring patients to an appropriate doctor, healthcare staff must have the patient’s symptoms. This study leverages the PhoBERT model to assist in classifying patients with text classification tasks based on symptoms they provided in the first stages of Vietnamese hospital admission. The outcomes of PhoBERT on more than 200 000 text-based symptoms collected from Vietnamese hospitals can improve the classification performance compared to Bag of Words (BOW) with classic machine learning algorithms, and some considered deep learning architectures such as 1D-Convolutional Neural Networks and Long Short-Term Memory. The proposed method can achieve promising results to be deployed in automatic hospital admission procedures in Vietnam.
{"title":"PhoBERT: Application in Disease Classification based on Vietnamese Symptom Analysis","authors":"Nguyen Ha Thanh, Tuyet Ngoc Huynh, Nhi Mai, K. D. Le, Pham Thi-Ngoc-Diem","doi":"10.2478/acss-2023-0004","DOIUrl":"https://doi.org/10.2478/acss-2023-0004","url":null,"abstract":"Abstract Besides the successful use of support software in cutting-edge medical procedures, the significance of determining a disease early signs and symptoms before its detection is a growing pressing requirement to raise the standard of medical examination and treatment. This creates favourable conditions, reduces patient inconvenience and hospital overcrowding. Before transferring patients to an appropriate doctor, healthcare staff must have the patient’s symptoms. This study leverages the PhoBERT model to assist in classifying patients with text classification tasks based on symptoms they provided in the first stages of Vietnamese hospital admission. The outcomes of PhoBERT on more than 200 000 text-based symptoms collected from Vietnamese hospitals can improve the classification performance compared to Bag of Words (BOW) with classic machine learning algorithms, and some considered deep learning architectures such as 1D-Convolutional Neural Networks and Long Short-Term Memory. The proposed method can achieve promising results to be deployed in automatic hospital admission procedures in Vietnam.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85605528","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}
Abstract The use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the smartphones. On the other hand, these small devices can have limited resources of computation power, battery backup, RAM memory, and storage space due to their small size. These devices need to reconcile resource hungry applications. This research focuses on solving issues of power and efficiency of smart devices by adapting intelligently to mobile usage by profiling the user intelligently. Our designed architecture makes a smartphone smarter by intelligently utilizing its resources to increase the battery life. Our developed application makes profiles of the applications usage at different time intervals. These stored usage profiles are utilized to make intelligent resource allocation for next time interval. We implemented and evaluated the profiling scheme for different brands of android smartphone. We implemented our approach with Naive Bayes and Decision Tree for performance and compared it with conventional approach. The results show that the proposed approach based on decision trees saves 31 % CPU and 60 % of RAM usage as compared to the conventional approach.
{"title":"Intelligent Mobile User Profiling for Maximum Performance","authors":"A. Muhammad, Sher Afghan, Afzal Muhammad","doi":"10.2478/acss-2023-0014","DOIUrl":"https://doi.org/10.2478/acss-2023-0014","url":null,"abstract":"Abstract The use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the smartphones. On the other hand, these small devices can have limited resources of computation power, battery backup, RAM memory, and storage space due to their small size. These devices need to reconcile resource hungry applications. This research focuses on solving issues of power and efficiency of smart devices by adapting intelligently to mobile usage by profiling the user intelligently. Our designed architecture makes a smartphone smarter by intelligently utilizing its resources to increase the battery life. Our developed application makes profiles of the applications usage at different time intervals. These stored usage profiles are utilized to make intelligent resource allocation for next time interval. We implemented and evaluated the profiling scheme for different brands of android smartphone. We implemented our approach with Naive Bayes and Decision Tree for performance and compared it with conventional approach. The results show that the proposed approach based on decision trees saves 31 % CPU and 60 % of RAM usage as compared to the conventional approach.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78497004","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}
Abstract The widespread use of social media all around the globe has affected the way of life in all aspects, not only for individuals but for businesses as well. Businesses share their upcoming events, reveal their products, and advertise to their potential customers, where individuals use social media to stay connected with their social circles, get updates and news from social media pages of news agencies, and update their information regarding the latest activities, businesses, economy, events, politics, trends, and about their area of interest. According to Statista, there were 4.59 billion users of social media worldwide in 2022 and expected to grow up to 5.85 billion in the year 2027. With its massive user base, social media does not only generate useful information for businesses and individuals, but at the same time, it also creates an abundance of misinformation and disinformation as well as malinformation to acquire social-political or business agendas. Individuals tend to share social media posts without checking the authenticity of the information they are sharing, which results in posts having misinformation, disinformation, or malinformation becoming viral around the world in a matter of minutes. Identifying misinformation, disinformation, and malinformation has become a prominent problem associated with social media.
{"title":"Social Media: An Exploratory Study of Information, Misinformation, Disinformation, and Malinformation","authors":"Mumtaz Hussain, Tariq Rahim Soomro","doi":"10.2478/acss-2023-0002","DOIUrl":"https://doi.org/10.2478/acss-2023-0002","url":null,"abstract":"Abstract The widespread use of social media all around the globe has affected the way of life in all aspects, not only for individuals but for businesses as well. Businesses share their upcoming events, reveal their products, and advertise to their potential customers, where individuals use social media to stay connected with their social circles, get updates and news from social media pages of news agencies, and update their information regarding the latest activities, businesses, economy, events, politics, trends, and about their area of interest. According to Statista, there were 4.59 billion users of social media worldwide in 2022 and expected to grow up to 5.85 billion in the year 2027. With its massive user base, social media does not only generate useful information for businesses and individuals, but at the same time, it also creates an abundance of misinformation and disinformation as well as malinformation to acquire social-political or business agendas. Individuals tend to share social media posts without checking the authenticity of the information they are sharing, which results in posts having misinformation, disinformation, or malinformation becoming viral around the world in a matter of minutes. Identifying misinformation, disinformation, and malinformation has become a prominent problem associated with social media.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135046043","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}
Abstract A problem of partitioning large datasets of flat points is considered. Known as the centroid-based clustering problem, it is mainly addressed by the k-means algorithm and its modifications. As the k-means performance becomes poorer on large datasets, including the dataset shape stretching, the goal is to study a possibility of improving the centroid-based clustering for such cases. It is quite noticeable on non-sparse datasets that the resulting clusters produced by k-means resemble beehive honeycomb. It is natural for rectangular-shaped datasets because the hexagonal cells make efficient use of space owing to which the sum of the within-cluster squared Euclidean distances to the centroids is approximated to its minimum. Therefore, the lattices of rectangular and hexagonal clusters, consisting of stretched rectangles and regular hexagons, are suggested to be successively applied. Then the initial centroids are calculated by averaging within respective hexagons. These centroids are used as initial seeds to start the k-means algorithm. This ensures faster and more accurate convergence, where at least the expected speedup is 1.7 to 2.1 times by a 0.7 to 0.9 % accuracy gain. The lattice of rectangular clusters applied first makes rather rough but effective partition allowing to optionally run further clustering on parallel processor cores. The lattice of hexagonal clusters applied to every rectangle allows obtaining initial centroids very quickly. Such centroids are far closer to the solution than the initial centroids in the k-means++ algorithm. Another approach to the k-means update, where initial centroids are selected separately within every rectangle hexagons, can be used as well. It is faster than selecting initial centroids across all hexagons but is less accurate. The speedup is 9 to 11 times by a possible accuracy loss of 0.3 %. However, this approach may outperform the k-means algorithm. The speedup increases as both the lattices become denser and the dataset becomes larger reaching 30 to 50 times.
{"title":"Speedup of the k-Means Algorithm for Partitioning Large Datasets of Flat Points by a Preliminary Partition and Selecting Initial Centroids","authors":"V. Romanuke","doi":"10.2478/acss-2023-0001","DOIUrl":"https://doi.org/10.2478/acss-2023-0001","url":null,"abstract":"Abstract A problem of partitioning large datasets of flat points is considered. Known as the centroid-based clustering problem, it is mainly addressed by the k-means algorithm and its modifications. As the k-means performance becomes poorer on large datasets, including the dataset shape stretching, the goal is to study a possibility of improving the centroid-based clustering for such cases. It is quite noticeable on non-sparse datasets that the resulting clusters produced by k-means resemble beehive honeycomb. It is natural for rectangular-shaped datasets because the hexagonal cells make efficient use of space owing to which the sum of the within-cluster squared Euclidean distances to the centroids is approximated to its minimum. Therefore, the lattices of rectangular and hexagonal clusters, consisting of stretched rectangles and regular hexagons, are suggested to be successively applied. Then the initial centroids are calculated by averaging within respective hexagons. These centroids are used as initial seeds to start the k-means algorithm. This ensures faster and more accurate convergence, where at least the expected speedup is 1.7 to 2.1 times by a 0.7 to 0.9 % accuracy gain. The lattice of rectangular clusters applied first makes rather rough but effective partition allowing to optionally run further clustering on parallel processor cores. The lattice of hexagonal clusters applied to every rectangle allows obtaining initial centroids very quickly. Such centroids are far closer to the solution than the initial centroids in the k-means++ algorithm. Another approach to the k-means update, where initial centroids are selected separately within every rectangle hexagons, can be used as well. It is faster than selecting initial centroids across all hexagons but is less accurate. The speedup is 9 to 11 times by a possible accuracy loss of 0.3 %. However, this approach may outperform the k-means algorithm. The speedup increases as both the lattices become denser and the dataset becomes larger reaching 30 to 50 times.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83329766","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}
Abstract In this paper, we propose a new approach, based on bigraphic reactive systems (BRS), to provide a formal modelling of the architectural elements of a Multi-Viewpoints ontology (MVp ontology). We introduce a formal model in which the main elements of MVp ontology find their definition in terms of bigraphic concepts by preserving their semantics. Besides, we enrich the proposed model with reaction rules in order to handle the dynamic reactions of MVp ontology. In order to confirm the applicability of our approach, we have carried out a case study using the proposed model.
{"title":"BRS-based Model for the Specification of Multi-view Point Ontology","authors":"Manel Kolli","doi":"10.2478/acss-2023-0008","DOIUrl":"https://doi.org/10.2478/acss-2023-0008","url":null,"abstract":"Abstract In this paper, we propose a new approach, based on bigraphic reactive systems (BRS), to provide a formal modelling of the architectural elements of a Multi-Viewpoints ontology (MVp ontology). We introduce a formal model in which the main elements of MVp ontology find their definition in terms of bigraphic concepts by preserving their semantics. Besides, we enrich the proposed model with reaction rules in order to handle the dynamic reactions of MVp ontology. In order to confirm the applicability of our approach, we have carried out a case study using the proposed model.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77653845","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}
Abstract Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out on three biometric databases. The results show that the proposed strategy performs very encouragingly, especially in combination with Empirical Modal Decomposition (EMD). The proposed fusion system shows good performance. The best result is obtained by merging the globality online signature and fingerprint where an EER of 1.69 % is obtained by normalizing the scores according to the Min-Max method.
{"title":"Multimodal Biometric System Based on the Fusion in Score of Fingerprint and Online Handwritten Signature","authors":"T. Hafs, Hatem Zehir, A. Hafs, A. Nait-Ali","doi":"10.2478/acss-2023-0006","DOIUrl":"https://doi.org/10.2478/acss-2023-0006","url":null,"abstract":"Abstract Multimodal biometrics is the technique of using multiple modalities on a single system. This allows us to overcome the limitations of unimodal systems, such as the inability to acquire data from certain individuals or intentional fraud, while improving recognition performance. In this paper, a study of score normalization and its impact on the performance of the system is performed. The fusion of scores requires prior normalisation before applying a weighted sum fusion that separates impostor and genuine scores into a common interval with close ranges. The experiments were carried out on three biometric databases. The results show that the proposed strategy performs very encouragingly, especially in combination with Empirical Modal Decomposition (EMD). The proposed fusion system shows good performance. The best result is obtained by merging the globality online signature and fingerprint where an EER of 1.69 % is obtained by normalizing the scores according to the Min-Max method.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72934542","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}
Abstract Sentiment analysis (SA) has been an important focus of study in the fields of computational linguistics and data analysis for a decade. Recently, promising results have been achieved when applying DNN models to sentiment analysis tasks. Long short-term memory (LSTM) models, as well as its derivatives like gated recurrent unit (GRU), are becoming increasingly popular in neural architecture used for sentiment analysis. Using these models in the feature extraction layer of a DNN results in a high dimensional feature space, despite the fact that the models can handle sequences of arbitrary length. Another problem with these models is that they weight each feature equally. Natural language processing (NLP) makes use of word embeddings created with word2vec. For many NLP jobs, deep neural networks have become the method of choice. Traditional deep networks are not dependable in storing contextual information, so dealing with sequential data like text and sound was a nightmare for such networks. This research proposes multichannel word embedding and employing stack of neural networks with lexicon-based padding and attention mechanism (MCSNNLA) method for SA. Using convolution neural network (CNN), Bi-LSTM, and the attention process in mind, this approach to sentiment analysis is described. One embedding layer, two convolution layers with max-pooling, one LSTM layer, and two fully connected (FC) layers make up the proposed technique, which is tailored for sentence-level SA. To address the shortcomings of prior SA models for product reviews, the MCSNNLA model integrates the aforementioned sentiment lexicon with deep learning technologies. The MCSNNLA model combines the strengths of emotion lexicons with those of deep learning. To begin, the reviews are processed with the sentiment lexicon in order to enhance the sentiment features. The experimental findings show that the model has the potential to greatly improve text SA performance.
{"title":"Multichannel Approach for Sentiment Analysis Using Stack of Neural Network with Lexicon Based Padding and Attention Mechanism","authors":"V. R. Kota, Munisamy Shyamala Devi","doi":"10.2478/acss-2023-0013","DOIUrl":"https://doi.org/10.2478/acss-2023-0013","url":null,"abstract":"Abstract Sentiment analysis (SA) has been an important focus of study in the fields of computational linguistics and data analysis for a decade. Recently, promising results have been achieved when applying DNN models to sentiment analysis tasks. Long short-term memory (LSTM) models, as well as its derivatives like gated recurrent unit (GRU), are becoming increasingly popular in neural architecture used for sentiment analysis. Using these models in the feature extraction layer of a DNN results in a high dimensional feature space, despite the fact that the models can handle sequences of arbitrary length. Another problem with these models is that they weight each feature equally. Natural language processing (NLP) makes use of word embeddings created with word2vec. For many NLP jobs, deep neural networks have become the method of choice. Traditional deep networks are not dependable in storing contextual information, so dealing with sequential data like text and sound was a nightmare for such networks. This research proposes multichannel word embedding and employing stack of neural networks with lexicon-based padding and attention mechanism (MCSNNLA) method for SA. Using convolution neural network (CNN), Bi-LSTM, and the attention process in mind, this approach to sentiment analysis is described. One embedding layer, two convolution layers with max-pooling, one LSTM layer, and two fully connected (FC) layers make up the proposed technique, which is tailored for sentence-level SA. To address the shortcomings of prior SA models for product reviews, the MCSNNLA model integrates the aforementioned sentiment lexicon with deep learning technologies. The MCSNNLA model combines the strengths of emotion lexicons with those of deep learning. To begin, the reviews are processed with the sentiment lexicon in order to enhance the sentiment features. The experimental findings show that the model has the potential to greatly improve text SA performance.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76732839","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}
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