International Journal of Interactive Multimedia and Artificial Intelligence最新文献_第2页

Development of an Intelligent Classifier Model for Denial of Service Attack Detection 拒绝服务攻击检测智能分类器模型的开发

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.08.003

Álvaro Michelena, Jose Aveleira-Mata, Esteban Jove, Héctor Alaiz-Moretón, Héctor Quintián, José Luis Calvo-Rolle

{"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":null,"pages":null},"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}

引用次数: 0

Editor’s Note Editor’s音符

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.08.008

Ricardo S. Alonso, Pablo Chamoso, Sara Rodríguez-González, Paulo Novais

引用次数: 0

RIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs RIADA:一种基于机器学习的基础设施，用于识别Spotify歌曲的情感

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2022.04.002

P. Álvarez, J. García de Quirós, S. Baldassarri

引用次数: 1

Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering 基于模糊聚类的案例推理系统检索性能改进

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.07.002

F. Saadi, Baghdad Atmani, F. Henni

引用次数: 0

An Investigation Into Different Text Representations to Train an Artificial Immune Network for Clustering Texts 基于不同文本表示训练人工免疫网络聚类的研究

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.08.006

Matheus A. Ferraria, Vinicius A. Ferraria, Leandro N. de Castro

{"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":null,"pages":null},"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}

引用次数: 0

Automatic Cell Counting With YOLOv5: A Fluorescence Microscopy Approach 自动细胞计数与YOLOv5:一种荧光显微镜方法

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.08.001

Sebastián López Flórez, Alfonso González-Briones, Guillermo Hernández, Carlos Ramos, Fernando de la Prieta

{"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":null,"pages":null},"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}

引用次数: 0

Large Language Models for in Situ Knowledge Documentation and Access With Augmented Reality 大型语言模型的现场知识文档和访问与增强现实

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.09.002

Juan Izquierdo-Domenech, Jordi Linares-Pellicer, Isabel Ferri-Molla

引用次数: 0

Point Cloud Deep Learning Solution for Hand Gesture Recognition 手势识别的点云深度学习解决方案

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.01.001

César Osimani, Juan Jesus Ojeda-Castelo, Jose A. Piedra-Fernandez

引用次数: 1

Synthetic Aperture Radar Automatic Target Recognition Based on a Simple Attention Mechanism 基于简单注意机制的合成孔径雷达目标自动识别

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 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

引用次数: 1

Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach 测量来自受控和非受控来源的图片之间的差异，以促进目的地。深度学习方法

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.10.003

Angel Diaz-Pacheco, Miguel A. Álvarez-Carmona, Ansel Y. Rodríguez-González, Hugo Carlos, Ramón Aranda

{"title":"Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach","authors":"Angel Diaz-Pacheco, Miguel A. Álvarez-Carmona, Ansel Y. Rodríguez-González, Hugo Carlos, Ramón Aranda","doi":"10.9781/ijimai.2023.10.003","DOIUrl":"https://doi.org/10.9781/ijimai.2023.10.003","url":null,"abstract":"Promoting a destination is a major task for Destination Marketing Organizations (DMOs). Although DMOs control, to some extent, the information presented to travelers (controlled sources), there are other different sources of information (uncontrolled sources) that could project an unfavorable image of the destination. Measuring differences between information sources would help design strategies to mitigate negative factors. In this way, we propose a deep learning-based approach to automatically measure the changes between images from controlled and uncontrolled information sources. Our approach exempts experts from the time-consuming task of assessing enormous quantities of pictures to track changes. To our best knowledge, this work is the first work that focuses on this issue using technological paradigms. Notwithstanding this, our approach paves novel pathways to acquire strategic insights that can be harnessed for the augmentation of destination development, the refinement of recommendation systems, the analysis of online travel reviews, and myriad other pertinent domains.","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135445921","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}

引用次数: 0