2022 IEEE World AI IoT Congress (AIIoT)最新文献_第10页

Low-Cost Ensembling for Deep Neural Network based Non-Intrusive Load Monitoring 基于深度神经网络的低成本非侵入式负荷监测

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817165

B. Gowrienanthan, N. Kiruthihan, K. Rathnayake, S. Kumarawadu, V. Logeeshan

引用次数: 3

Deep Learning Models for Water Potability Classification in Rural Areas in the Philippines 菲律宾农村地区饮用水分类的深度学习模型

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817352

Argeen Blanco, Lance Victor Del Rosario, Ken Ichiro Jose, Melchizedek I. Alipio

{"title":"Deep Learning Models for Water Potability Classification in Rural Areas in the Philippines","authors":"Argeen Blanco, Lance Victor Del Rosario, Ken Ichiro Jose, Melchizedek I. Alipio","doi":"10.1109/aiiot54504.2022.9817352","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817352","url":null,"abstract":"According to the World Bank, one out of five Filipinos do not get water from formal sources. Only 77% of the rural population and 90% of those in urban areas have access to an improved water source and only 44% have direct house connections. Surveillance of water quality is mandatory thus many research studies have been presented to different communities that showed effective results. In rural areas, there is already a classification model for water potability using traditional machine learning techniques. However, there currently no deep learning-based model for water potability classification. Thus, this work aims to create a deep learning water potability classification model for rural water sources in the Philippines. It starts from importing the water potability dataset of water monitoring sources from rural areas then pre-processing of the data, evaluation of the performance of the learning models through accuracy, precision, recall and f-measure metrics. Out of all the three, MLP had provided the greatest accuracy of 99.80%. LSTM performed better in accuracy and recall in comparison to GRU, but GRU had provided better precision than LSTM. LSTM has been considered to greatly classify the most common classifications in the dataset, while GRU has been observed to accurately classify the infrequent classifications in the dataset.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"327 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134057038","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

Development of Cloud-based Infrastructure for Real Time Analysis of Wearable Sensor Signal 基于云的可穿戴传感器信号实时分析基础设施的开发

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817145

Kabir Hossain, Tonmoy Ghosh, E. Sazonov

{"title":"Development of Cloud-based Infrastructure for Real Time Analysis of Wearable Sensor Signal","authors":"Kabir Hossain, Tonmoy Ghosh, E. Sazonov","doi":"10.1109/aiiot54504.2022.9817145","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817145","url":null,"abstract":"This paper focuses on development of server-based infrastructure for real-time analysis of wearable signals. In this work, we have implemented a python flask-based API (Application Programming Interface) to receive sensor and image data from various platforms (e.g., mobile, computer), and created a data storage (MariaDB database and file server) to store data. A load balancer, Nginx, that redirects traffic into different ports was configured for low latency. Additionally, we developed a food intake detection method based on machine learning (ML). We have investigated ten different ML models to find an accurate and fast model. To test the server infrastructure, we conducted a functionality test to verify each component of the server. We also investigated how a number of APIs influence the performance of the server in terms of latency. To verify the server, we performed a computer simulation where a python script was used to deliver signals and images continuously to the server. We sent a total of five hundred images and sensor signals to the server from two different processes simultaneously. We achieved an average latency of 260ms and 110ms for signal and image packets, respectively. The average latency decreased by 26.92% and 15.38% when we use two API ports. For food intake detections, data were collected from 17 free-living (9 males, 6 females, and 2 adolescents) volunteers. Thereafter these data were evaluated by ten different ML classifiers, e.g., Adaboost (AB), Random Forest (RF), Gradient Boosting (GB) and Histogram Gradient Boosting (HGB). The experiments were performed by 5-fold validations, where 80% of subjects were used for training the remaining 20% for testing. The RF model provided the best result with average accuracy, precision, recall and F1-score of 0.99, 0.97, 0.97 and 0.98, respectively. Results indicate that our implemented server architecture was able to receive signals in real-time and detect food intake with high accuracy.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"6 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132545542","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

Extraction of Step Performed in Use Case Description as a Reference for Conformity of Sequence Diagrams Using Text Mining (Case Study: SRS APTU) 使用文本挖掘提取用例描述中执行的步骤作为序列图一致性的参考(案例研究:SRS APTU)

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817341

Nur Apriyanto, Y. Priyadi, D. S. Kusumo

{"title":"Extraction of Step Performed in Use Case Description as a Reference for Conformity of Sequence Diagrams Using Text Mining (Case Study: SRS APTU)","authors":"Nur Apriyanto, Y. Priyadi, D. S. Kusumo","doi":"10.1109/aiiot54504.2022.9817341","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817341","url":null,"abstract":"Extraction is an essential part of processing a document to ensure the success of the text mining process. In this study, the example of the SRS document used is the Integrated Service Application (APTU) KPKNL Bandung, an application to manage the process of submitting service tickets at the State Property and Auction Service Office. There is a difference in interpreting the activities that exist in the Use Case Description artifact with a Sequence Diagram that provides an overview of the functionality of a process to show the involvement of an activity related to the Use Case Description. This study aims to perform step extraction on the Use Case description. The results of this extraction are compared for their suitability with the sequence diagram using the concept of text mining. There are core results from this research activity. First, the highest similarity between documents is in the SP01 and SD01 documents, with the similarity value being 0.69108792. Second, the highest similarity between words is found in words “list” and “menu,” with the similarity value being 0.9412. Third, the Kappa Score from Gwet's AC1 formula using the Python programming language is 0.12362, which means “Slight Agreement,” while the Kappa Score value using a questionnaire filled in by the expert is 0.97464, which means “Almost perfect.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129353281","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: 7

Fake Product Review Detection Using Machine Learning 使用机器学习的假产品评论检测

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817271

Md Mahadi Hassan Sohan, Mohammad Monirujjaman Khan, Ipseeta Nanda, Rajesh Dey

{"title":"Fake Product Review Detection Using Machine Learning","authors":"Md Mahadi Hassan Sohan, Mohammad Monirujjaman Khan, Ipseeta Nanda, Rajesh Dey","doi":"10.1109/aiiot54504.2022.9817271","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817271","url":null,"abstract":"Online reviews play a crucial role in determining whether a product will be sold on e-commerce websites or applications. Because so many people rely on internet evaluations, unethical actors may fabricate reviews in order to artificially boost or devalue items and services. To detect false product reviews, this research provides a semi-supervised machine learning approach. Furthermore, feature engineering techniques are used in this work to extract diverse reviewer behaviors. This study examines the outcomes of numerous experiments on a real food review dataset of restaurant reviews with attributes collected from user behavior. In terms off-score, the results indicate that Random Forest surpasses another classifier, with the best f-score of 98 %. In addition, the data reveals that taking into account the reviewers' behavioral characteristics raises the f-score and the final accuracy has come out 97.7%. In the current technique, not all reviewers' behavioral characteristics have been considered. Other low-level features such as frequent time or date dependency, the reviewer's timing for giving a review, and how common it is to deliver favorable or poor reviews will be added further in order to improve the efficacy of the offered fake review detecting algorithm.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134545628","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: 2

Electronic device for acquiring images of sardine cans 获取沙丁鱼罐头图象的电子装置

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817260

Salah-Eddine Mansour, Abdelhak Sakhi, Larbi Kzaz, Amine Erroutbi, A. Sekkaki

引用次数: 0

Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks 基于生成对抗网络的工程文档船舶甲板分割

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817355

M. Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa-Poza, M. Canan, Samuel F. Kovacic, Jiang Li

引用次数: 3

Leveraging Transfer Learning and GAN Models for OCR from Engineering Documents 利用迁移学习和GAN模型从工程文档中进行OCR

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817319

Wael Khallouli, Raphael Pamie-George, Samuel F. Kovacic, A. Sousa-Poza, M. Canan, Jiang Li

{"title":"Leveraging Transfer Learning and GAN Models for OCR from Engineering Documents","authors":"Wael Khallouli, Raphael Pamie-George, Samuel F. Kovacic, A. Sousa-Poza, M. Canan, Jiang Li","doi":"10.1109/aiiot54504.2022.9817319","DOIUrl":"https://doi.org/10.1109/aiiot54504.2022.9817319","url":null,"abstract":"Digital engineering, the digital transformation of engineering practice, is profoundly changing the traditional engineering practice towards the fast integration of digital technologies and digital models in the engineering processes' life cycles. The traditional engineering process heavily relies on static engineering documents (e.g., spreadsheets, technical drawings, and scanned documents) to store and share information across the engineering process. A critical task in digital engineering is to extract relevant textual information from traditional engineering documents into machine-readable and editable formats. This paper explores deep learning models and OCR methods to effectively extract textual information from engineering documents collected by the NAVY's military sealift command division. We propose a deep learning-based optical character recognition (OCR) framework for this task, which integrates several modules including a pre-trained text detection model, a fine-tuned OCR algorithm, and a deep generative model to augment data for the fine-tuning. Experimental results showed that the fine-tuning method significantly improved word accuracies of OCR models from 60%-70% to 90% and above. Furthermore, the deep adversarial generative approach had proved to be an effective model for data augmentation.","PeriodicalId":409264,"journal":{"name":"2022 IEEE World AI IoT Congress (AIIoT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116738483","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: 5

CNN-Based Hyperparameter Optimization Approach for Road Pothole and Crack Detection Systems 基于cnn的路面坑洞与裂缝检测系统超参数优化方法

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817316

Zahra Salsabila Hernanda, H. Mahmudah, R. Sudibyo

引用次数: 4

An Evaluation and Embedded Hardware Implementation of YOLO for Real-Time Wildfire Detection YOLO实时野火探测系统的评估与嵌入式硬件实现

2022 IEEE World AI IoT Congress (AIIoT) Pub Date : 2022-06-06 DOI: 10.1109/aiiot54504.2022.9817206

Jordan Johnston, Kaiman Zeng, Nansong Wu

引用次数: 1