International Journal of Interactive Multimedia and Artificial Intelligence最新文献_第3页

What Do We Mean by GenAI? A Systematic Mapping of The Evolution, Trends, and Techniques Involved in Generative AI GenAI指的是什么?生成式人工智能的进化、趋势和技术的系统映射

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.07.006

Francisco García-Peñalvo, Andrea Vázquez-Ingelmo

引用次数: 2

S-Divergence-Based Internal Clustering Validation Index 基于s -散度的内部聚类验证指标

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.10.001

Krishna Kumar Sharma, Ayan Seal, Anis Yazidi, Ondrej Krejcar

引用次数: 0

Violence Detection in Audio: Evaluating the Effectiveness of Deep Learning Models and Data Augmentation 音频中的暴力检测:评估深度学习模型和数据增强的有效性

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.08.007

Dalila Durães, Bruno Veloso, Paulo Novais

{"title":"Violence Detection in Audio: Evaluating the Effectiveness of Deep Learning Models and Data Augmentation","authors":"Dalila Durães, Bruno Veloso, Paulo Novais","doi":"10.9781/ijimai.2023.08.007","DOIUrl":"https://doi.org/10.9781/ijimai.2023.08.007","url":null,"abstract":"Human nature is inherently intertwined with violence, impacting the lives of numerous individuals. Various forms of violence pervade our society, with physical violence being the most prevalent in our daily lives. The study of human actions has gained significant attention in recent years, with audio (captured by microphones) and video (captured by cameras) being the primary means to record instances of violence. While video requires substantial processing capacity and hardware-software performance, audio presents itself as a viable alternative, offering several advantages beyond these technical considerations. Therefore, it is crucial to represent audio data in a manner conducive to accurate classification. In the context of violence in a car, specific datasets dedicated to this domain are not readily available. As a result, we had to create a custom dataset tailored to this particular scenario. The purpose of curating this dataset was to assess whether it could enhance the detection of violence in car-related situations. Due to the imbalanced nature of the dataset, data augmentation techniques were implemented. Existing literature reveals that Deep Learning (DL) algorithms can effectively classify audio, with a commonly used approach involving the conversion of audio into a mel spectrogram image. Based on the results obtained for that dataset, the EfficientNetB1 neural network demonstrated the highest accuracy (95.06%) in detecting violence in audios, closely followed by EfficientNetB0 (94.19%). Conversely, MobileNetV2 proved to be less capable in classifying instances of violence.","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"15 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":"135312749","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

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":"26 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":"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

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

Pollutant Time Series Analysis for Improving Air-Quality in Smart Cities 改善智慧城市空气质量的污染物时间序列分析

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.08.005

Raúl López-Blanco, Miguel Chaveinte García, Ricardo S. Alonso, Javier Prieto, Juan M. Corchado

{"title":"Pollutant Time Series Analysis for Improving Air-Quality in Smart Cities","authors":"Raúl López-Blanco, Miguel Chaveinte García, Ricardo S. Alonso, Javier Prieto, Juan M. Corchado","doi":"10.9781/ijimai.2023.08.005","DOIUrl":"https://doi.org/10.9781/ijimai.2023.08.005","url":null,"abstract":"The evolution towards Smart Cities is the process that many urban centers are following in their quest for efficiency, resource optimization and sustainable growth. This step forward in the continuous improvement of cities is closely linked to the quality of life they want to offer their citizens. One of the key issues that can have the greatest impact on the quality of life of all city dwellers is the quality of the air they breathe, which can lead to illnesses caused by pollutants in the air. The application of new technologies, such as the Internet of Things, Big Data and Artificial Intelligence, makes it possible to obtain increasingly abundant and accurate data on what is happening in cities, providing more information to take informed action based on scientific data. This article studies the evolution of pollutants in the main cities of Castilla y León, using Generative Additive Models (GAM), which have proven to be the most efficient for making predictions with detailed historical data and which have very strong seasonalities. The results of this study conclude that during the COVID-19 pandemic containment period, there was an overall reduction in the concentration of pollutants.","PeriodicalId":48602,"journal":{"name":"International Journal of Interactive Multimedia and Artificial Intelligence","volume":"91 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":"134989267","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

PeopleNet: A Novel People Counting Framework for Head-Mounted Moving Camera Videos PeopleNet:一种新颖的头戴式移动摄像机视频计数框架

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.04.002

Tomar Ankit, Kumar Santosh, Pant Bhasker

引用次数: 1

ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System 卷积神经网络cnn:视频监控系统中真实世界异常检测的时空深度学习

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.05.006

Maryam Qasim Gandapur, Elena Verdú

{"title":"ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System","authors":"Maryam Qasim Gandapur, Elena Verdú","doi":"10.9781/ijimai.2023.05.006","DOIUrl":"https://doi.org/10.9781/ijimai.2023.05.006","url":null,"abstract":"Video surveillance for real-world anomaly detection and prevention using deep learning is an important and difficult research area. It is imperative to detect and prevent anomalies to develop a nonviolent society. Real-world video surveillance cameras automate the detection of anomaly activities and enable the law enforcement systems for taking steps toward public safety. However, a human-monitored surveillance system is vulnerable to oversight anomaly activity. In this paper, an automated deep learning model is proposed in order to detect and prevent anomaly activities. The real-world video surveillance system is designed by implementing the ResNet-50, a Convolutional Neural Network (CNN) model, to extract the high-level features from input streams whereas temporal features are extracted by the Convolutional GRU (ConvGRU) from the ResNet-50 extracted features in the time-series dataset. The proposed deep learning video surveillance model (named ConvGRU-CNN) can efficiently detect anomaly activities. The UCF-Crime dataset is used to evaluate the proposed deep learning model. We classified normal and abnormal activities, thereby showing the ability of ConvGRU-CNN to find a correct category for each abnormal activity. With the UCF-Crime dataset for the video surveillance-based anomaly detection, ConvGRU-CNN achieved 82.22% accuracy. In addition, the proposed model outperformed the related deep learning models.","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":"135685950","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: 1

OntoInfoG++: A Knowledge Fusion Semantic Approach for Infographics Recommendation 面向信息图推荐的知识融合语义方法

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2021.12.005

Gerard Deepak, Adithya Vibakar, A. Santhanavijayan

引用次数: 1

A Survey on Demand-Responsive Transportation for Rural and Interurban Mobility 农村和城际交通需求响应性交通调查

3区计算机科学

International Journal of Interactive Multimedia and Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.9781/ijimai.2023.07.010

Pasqual Martí, Jaume Jordán, María Angélica González Arrieta, Vicente Julian

引用次数: 0