2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)最新文献

Inverse Transform Sampling for Bibliometric Literature Analysis 文献计量学文献分析的逆变换抽样

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904344

N. Bakas, Dionisios Koutsantonis, V. Plevris, A. Langousis, S. Chatzichristofis

引用次数: 0

Analyzing Perceptual Picture Quality of Various Tone-Mapping Methods for Mobile Devices 各种色调映射方法在移动设备上的感知图像质量分析

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904391

C. Lee, S. Lee, S. Woo, J. Yoon

引用次数: 0

Live Vessels’ Monitoring using Geographic Information and Internet of Things 利用地理信息和物联网对活船进行监测

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904408

Ioannis Filippopoulos, T. Panagiotakopoulos, Charalampos Skiadas, Sofia-Michaela Triantafyllou, A. Violaris, Y. Kiouvrekis

引用次数: 2

Upgrading and testing of the UPV/EHU Stellarator UPV/EHU仿星器的升级与测试

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904379

A. Garrido, A. Marco, M. Sen, Í. Aguirre, I. G. Hernandez

引用次数: 0

Deep Neural Network Based Methodology for Very-Short-Term Residential Load Forecasting 基于深度神经网络的极短期住宅负荷预测方法

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904338

R. Gonzalez, Sara Ahmed, M. Alamaniotis

{"title":"Deep Neural Network Based Methodology for Very-Short-Term Residential Load Forecasting","authors":"R. Gonzalez, Sara Ahmed, M. Alamaniotis","doi":"10.1109/IISA56318.2022.9904338","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904338","url":null,"abstract":"Residential load forecasting has long been a prediction problem due to high uncertainty associated with a single electricity consumer. Predicting residential load demand is important for the efficient operation of utility networks and may consist of the network for the future interactive structure of electricity markets. In the past, many different machine learning algorithms have been applied for load forecasting including deep neural networks for various prediction horizons. However, smart grids and interactive markets will require prediction in very-short term horizons-in the scale of minutes-. This paper seeks to study the use of deep neural networks in very-short-term residential load forecasting. To that end a deep neural network is created and being trained on four different training datasets in order to observe the effect of the training on the network forecast ability. Forecast performance is measured with respect to mean average percentage error on a yearly long 5 min load values of a residential building. The results exhibit that that the deep network is able to make forecasts with MAPE laying in the interva11.1%-1.4% for all the four different training datasets.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124537742","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}

引用次数: 1

AI, Blockchain & Cyber tourism joining the Smart Tourism realm 人工智能、区块链和网络旅游加入智慧旅游领域

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904393

Aristea Kontogianni, Efthymios Alepis

引用次数: 0

Automated Recognition of healthy Anterior Cruciate Ligament in Sagittal MR images using Lightweight Deep Learning 基于轻量级深度学习的矢状面磁共振图像健康前交叉韧带自动识别

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904387

A. Siouras, S. Moustakidis, A. Giannakidis, G. Chalatsis, K. Malizos, M. Hantes, Sotiris K. Tasoulis, D. Tsaopoulos

{"title":"Automated Recognition of healthy Anterior Cruciate Ligament in Sagittal MR images using Lightweight Deep Learning","authors":"A. Siouras, S. Moustakidis, A. Giannakidis, G. Chalatsis, K. Malizos, M. Hantes, Sotiris K. Tasoulis, D. Tsaopoulos","doi":"10.1109/IISA56318.2022.9904387","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904387","url":null,"abstract":"Anterior cruciate ligament (ACL) tears are very common among athletes. The success of enhanced ACL injury therapy hinges on accurate and cost-effective detection. Deep learning-based techniques have recently dominated ACL injury detection in MRI research. The goal of this study is to develop a robust and lightweight deep learning pipeline for identifying ACL in 3D MRI data of healthy knees. Specifically, we aim at finding the slices in the sagittal plane where the ACL is present. This could be utilized by clinicians for further evaluation. To this end, we build and test an advanced pipeline that relies on the newest object detection network, YOLOv5-Nano. We go on to compare our model to other pipelines that rely on YOLOv5-xlarge, YOLOX-small and YOLOX-nano. YOLOv5-nano is shown to be the best performer, obtaining the highest overall mAP@0.5 performance (0.9727) on augmented data, while at the same time having the smallest model size (3.7 MB). Conclusive object detection is a key step in identifying damage. YOLOv5-nano offers a great solution towards achieving robust object detection healthcare systems that will permit local processing by devices with limited computational resources.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132560443","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}

引用次数: 1

Digital Modelling of Historical Buildings: The case of Kapodistrian Aegina 历史建筑的数字化建模:以埃伊纳岛Kapodistrian为例

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904389

Kalliopi Papandreou, D. Vergados

引用次数: 0

Sleep Quality Evaluation in Rich Information Data 基于丰富信息数据的睡眠质量评价

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904403

Ioannis Konstantoulas, Elias Dritsas, K. Moustakas

引用次数: 3

A Machine Learning Approach for Modeling Time-Varying Hit Song Preferences 时变热门歌曲偏好建模的机器学习方法

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904376

Dionisios Nikas, Dionisios N. Sotiropoulos

{"title":"A Machine Learning Approach for Modeling Time-Varying Hit Song Preferences","authors":"Dionisios Nikas, Dionisios N. Sotiropoulos","doi":"10.1109/IISA56318.2022.9904376","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904376","url":null,"abstract":"The music industry is investing each year huge amounts of money to artists and their songs with the ultimate goal of becoming a hit song. Since its creation back in 1958, the Billboard Weekly Hot 100 chart is one of the most iconic and reliable sources of hit songs. Using Spotify’s Web API and Genius API and their massive collection of songs we gathered all the high-level audio features, lyrics and some temporal features for all the songs that made it to the Hot 100 Chart in the period 1958-2020. Using these features, we will perform an analysis on the time varying preferences on what is considered a hit song using One-Class-SVM and conclude that most of the hit songs are very similar based on their high-level audio features and lyric word-embeddings. Then, to support our results and hypothesis even more, we will try to build a multi-class classifier using algorithms such as Random Forest, KNN, Logistic regression and Support Vector Machines (SVC with RBF kernel) to predict the position/popularity of a hit song on the billboard chart. Finally, we will address our thoughts on why these features may or may not be enough to build a hit song classifier and discuss future work for a better approach to this problem.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115668850","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}

引用次数: 0