Pub Date : 2023-01-01DOI: 10.9781/ijimai.2022.04.002
P. Álvarez, J. García de Quirós, S. Baldassarri
{"title":"RIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs","authors":"P. Álvarez, J. García de Quirós, S. Baldassarri","doi":"10.9781/ijimai.2022.04.002","DOIUrl":"https://doi.org/10.9781/ijimai.2022.04.002","url":null,"abstract":"","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135235904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.08.003
Álvaro Michelena, Jose Aveleira-Mata, Esteban Jove, Héctor Alaiz-Moretón, Héctor Quintián, José Luis Calvo-Rolle
The prevalence of Internet of Things (IoT) systems deployment is increasing across various domains, from residential to industrial settings. These systems are typically characterized by their modest computational requirements and use of lightweight communication protocols, such as MQTT. However, the rising adoption of IoT technology has also led to the emergence of novel attacks, increasing the susceptibility of these systems to compromise. Among the different attacks that can affect the main IoT protocols are Denial of Service attacks (DoS). In this scenario, this paper evaluates the performance of six supervised classification techniques (Decision Trees, Multi-layer Perceptron, Random Forest, Support Vector Machine, Fisher Linear Discriminant and Bernoulli and Gaussian Naive Bayes) combined with the Principal Component Analysis (PCA) feature extraction method for detecting DoS attacks in MQTT networks. For this purpose, a real dataset containing all the traffic generated in the network and many attacks executed has been used. The results obtained with several models have achieved performances above 99% AUC.
{"title":"Development of an Intelligent Classifier Model for Denial of Service Attack Detection","authors":"Álvaro Michelena, Jose Aveleira-Mata, Esteban Jove, Héctor Alaiz-Moretón, Héctor Quintián, José Luis Calvo-Rolle","doi":"10.9781/ijimai.2023.08.003","DOIUrl":"https://doi.org/10.9781/ijimai.2023.08.003","url":null,"abstract":"The prevalence of Internet of Things (IoT) systems deployment is increasing across various domains, from residential to industrial settings. These systems are typically characterized by their modest computational requirements and use of lightweight communication protocols, such as MQTT. However, the rising adoption of IoT technology has also led to the emergence of novel attacks, increasing the susceptibility of these systems to compromise. Among the different attacks that can affect the main IoT protocols are Denial of Service attacks (DoS). In this scenario, this paper evaluates the performance of six supervised classification techniques (Decision Trees, Multi-layer Perceptron, Random Forest, Support Vector Machine, Fisher Linear Discriminant and Bernoulli and Gaussian Naive Bayes) combined with the Principal Component Analysis (PCA) feature extraction method for detecting DoS attacks in MQTT networks. For this purpose, a real dataset containing all the traffic generated in the network and many attacks executed has been used. The results obtained with several models have achieved performances above 99% AUC.","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.08.008
Ricardo S. Alonso, Pablo Chamoso, Sara Rodríguez-González, Paulo Novais
{"title":"Editor’s Note","authors":"Ricardo S. Alonso, Pablo Chamoso, Sara Rodríguez-González, Paulo Novais","doi":"10.9781/ijimai.2023.08.008","DOIUrl":"https://doi.org/10.9781/ijimai.2023.08.008","url":null,"abstract":"","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.07.002
F. Saadi, Baghdad Atmani, F. Henni
{"title":"Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering","authors":"F. Saadi, Baghdad Atmani, F. Henni","doi":"10.9781/ijimai.2023.07.002","DOIUrl":"https://doi.org/10.9781/ijimai.2023.07.002","url":null,"abstract":"","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135102676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.08.006
Matheus A. Ferraria, Vinicius A. Ferraria, Leandro N. de Castro
Extracting knowledge from text data is a complex task that is usually performed by first structuring the texts and then applying machine learning algorithms, or by using specific deep architectures capable of dealing directly with the raw text data. The traditional approach to structure texts is called Bag of Words (BoW) and consists of transforming each word in a document into a dimension (variable) in the structured data. Another approach uses grammatical classes to categorize the words and, thus, limit the dimension of the structured data to the number of grammatical categories. Another form of structuring text data for analysis is by using a distributed representation of words, sentences, or documents with methods like Word2Vec, Doc2Vec, and SBERT. This paper investigates four classes of text structuring methods to prepare documents for being clustered by an artificial immune system called aiNet. The goal is to assess the influence of each structuring method in the quality of the clustering obtained by the system and how methods that belong to the same type of representation differ from each other, for example both LIWC and MRC are considered grammar-based models but each one of them uses completely different dictionaries to generate its representation. By using internal clustering measures, our results showed that vector space models, on average, presented the best results for the datasets chosen, followed closely by the state of the art SBERT model, and MRC had the overall worst performance. We could also observe a consistency in the number of clusters generated by each representation and for each dataset, having SBERT as the model that presented a number of clusters closer to the original number of classes in the data.
{"title":"An Investigation Into Different Text Representations to Train an Artificial Immune Network for Clustering Texts","authors":"Matheus A. Ferraria, Vinicius A. Ferraria, Leandro N. de Castro","doi":"10.9781/ijimai.2023.08.006","DOIUrl":"https://doi.org/10.9781/ijimai.2023.08.006","url":null,"abstract":"Extracting knowledge from text data is a complex task that is usually performed by first structuring the texts and then applying machine learning algorithms, or by using specific deep architectures capable of dealing directly with the raw text data. The traditional approach to structure texts is called Bag of Words (BoW) and consists of transforming each word in a document into a dimension (variable) in the structured data. Another approach uses grammatical classes to categorize the words and, thus, limit the dimension of the structured data to the number of grammatical categories. Another form of structuring text data for analysis is by using a distributed representation of words, sentences, or documents with methods like Word2Vec, Doc2Vec, and SBERT. This paper investigates four classes of text structuring methods to prepare documents for being clustered by an artificial immune system called aiNet. The goal is to assess the influence of each structuring method in the quality of the clustering obtained by the system and how methods that belong to the same type of representation differ from each other, for example both LIWC and MRC are considered grammar-based models but each one of them uses completely different dictionaries to generate its representation. By using internal clustering measures, our results showed that vector space models, on average, presented the best results for the datasets chosen, followed closely by the state of the art SBERT model, and MRC had the overall worst performance. We could also observe a consistency in the number of clusters generated by each representation and for each dataset, having SBERT as the model that presented a number of clusters closer to the original number of classes in the data.","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135312742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.08.001
Sebastián López Flórez, Alfonso González-Briones, Guillermo Hernández, Carlos Ramos, Fernando de la Prieta
Counting cells in a Neubauer chamber on microbiological culture plates is a laborious task that depends on technical experience. As a result, efforts have been made to advance computer vision-based approaches, increasing efficiency and reliability through quantitative analysis of microorganisms and calculation of their characteristics, biomass concentration, and biological activity. However, variability that still persists in these processes poses a challenge that is yet to be overcome. In this work, we propose a solution adopting a YOLOv5 network model for automatic cell recognition and counting in a case study for laboratory cell detection using images from a CytoSMART Exact FL microscope. In this context, a dataset of 21 expert-labeled cell images was created, along with an extra Sperm DetectionV dataset of 1024 images for transfer learning. The dataset was trained using the pre-trained YOLOv5 algorithm with the Sperm DetectionV database. A laboratory test was also performed to confirm result’s viability. Compared to YOLOv4, the current YOLOv5 model had accuracy, precision, recall, and F1 scores of 92%, 84%, 91%, and 87%, respectively. The YOLOv5 algorithm was also used for cell counting and compared to the current segmentation-based U-Net and OpenCV model that has been implemented. In conclusion, the proposed model successfully recognizes and counts the different types of cells present in the laboratory.
{"title":"Automatic Cell Counting With YOLOv5: A Fluorescence Microscopy Approach","authors":"Sebastián López Flórez, Alfonso González-Briones, Guillermo Hernández, Carlos Ramos, Fernando de la Prieta","doi":"10.9781/ijimai.2023.08.001","DOIUrl":"https://doi.org/10.9781/ijimai.2023.08.001","url":null,"abstract":"Counting cells in a Neubauer chamber on microbiological culture plates is a laborious task that depends on technical experience. As a result, efforts have been made to advance computer vision-based approaches, increasing efficiency and reliability through quantitative analysis of microorganisms and calculation of their characteristics, biomass concentration, and biological activity. However, variability that still persists in these processes poses a challenge that is yet to be overcome. In this work, we propose a solution adopting a YOLOv5 network model for automatic cell recognition and counting in a case study for laboratory cell detection using images from a CytoSMART Exact FL microscope. In this context, a dataset of 21 expert-labeled cell images was created, along with an extra Sperm DetectionV dataset of 1024 images for transfer learning. The dataset was trained using the pre-trained YOLOv5 algorithm with the Sperm DetectionV database. A laboratory test was also performed to confirm result’s viability. Compared to YOLOv4, the current YOLOv5 model had accuracy, precision, recall, and F1 scores of 92%, 84%, 91%, and 87%, respectively. The YOLOv5 algorithm was also used for cell counting and compared to the current segmentation-based U-Net and OpenCV model that has been implemented. In conclusion, the proposed model successfully recognizes and counts the different types of cells present in the laboratory.","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135312744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.09.002
Juan Izquierdo-Domenech, Jordi Linares-Pellicer, Isabel Ferri-Molla
{"title":"Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality","authors":"Juan Izquierdo-Domenech, Jordi Linares-Pellicer, Isabel Ferri-Molla","doi":"10.9781/ijimai.2023.09.002","DOIUrl":"https://doi.org/10.9781/ijimai.2023.09.002","url":null,"abstract":"","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135909721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.02.004
Chiagoziem C. Ukwuoma, Qin Zhiguang, Bole W. Tienin, Sophyani B. Yussif, Chukwuebuka J. Ejiyi, Gilbert C. Urama, Chibueze D. Ukwuoma, Ijeoma A. Chikwendu
{"title":"Synthetic Aperture Radar Automatic Target Recognition Based on a Simple Attention Mechanism","authors":"Chiagoziem C. Ukwuoma, Qin Zhiguang, Bole W. Tienin, Sophyani B. Yussif, Chukwuebuka J. Ejiyi, Gilbert C. Urama, Chibueze D. Ukwuoma, Ijeoma A. Chikwendu","doi":"10.9781/ijimai.2023.02.004","DOIUrl":"https://doi.org/10.9781/ijimai.2023.02.004","url":null,"abstract":"","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135585130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.9781/ijimai.2023.11.001
Rubén González-Sendino, Emilio Serrano, Javier Bajo, Paulo Novais
{"title":"A Review of Bias and Fairness in Artificial Intelligence","authors":"Rubén González-Sendino, Emilio Serrano, Javier Bajo, Paulo Novais","doi":"10.9781/ijimai.2023.11.001","DOIUrl":"https://doi.org/10.9781/ijimai.2023.11.001","url":null,"abstract":"","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135660992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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