2023 Intelligent Methods, Systems, and Applications (IMSA)最新文献_第6页

Generate Descriptions of Medical Dialogues Through Two-Layers Transformer-Based Summarization 通过基于双层变压器的摘要生成医学对话描述

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217636

George Elkess Abanoub, Ahmed Mahmoud Fawzy, Rana Reda Waly, W. H. Gomaa

{"title":"Generate Descriptions of Medical Dialogues Through Two-Layers Transformer-Based Summarization","authors":"George Elkess Abanoub, Ahmed Mahmoud Fawzy, Rana Reda Waly, W. H. Gomaa","doi":"10.1109/IMSA58542.2023.10217636","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217636","url":null,"abstract":"This paper presents a two-layer transformer-based summarization approach for generating descriptions of medical dialogues, with a focus on the concept that using small amounts of training data can produce promising results and avoid the need for high computing resources. Previous research has primarily utilized the full or most of the HealthCareMagic dataset, comprising 225,872 medical dialogues for training, while this study uses much smaller training datasets of 5000 and 7000 instances. Despite the smaller training sets, our approach yields promising results, with a challenging improvement of ROUGE evaluation metric up to 0.44 compared to previous work. The two-layer summarization technique involves the BART pre-trained transformer model for generating medical dialogue summaries, followed by fine-tuning the T5 pre-trained model to generate one-statement descriptions for each summary. This research has practical implications for improving medical dialogue summarization, enabling healthcare professionals to review and assess patient information more quickly.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134352923","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

Short Term Weather Forecasting Comparison Based on Machine Learning Algorithms 基于机器学习算法的短期天气预报比较

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217753

Chowdhury Abida Anjum Era, Mahmudur Rahman, Syada Tasmia Alvi

{"title":"Short Term Weather Forecasting Comparison Based on Machine Learning Algorithms","authors":"Chowdhury Abida Anjum Era, Mahmudur Rahman, Syada Tasmia Alvi","doi":"10.1109/IMSA58542.2023.10217753","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217753","url":null,"abstract":"Forecasting is the term used to describe the attempt to predict outcomes in unknown or uncertain situations. The most vital factor in many applications of weather forecasting is air temperature. The air temperature alone can’t be the effecting point of forecasting weather. Moreover, with the advancement of computer technologies, forecasting models have been transformed widely. This paper approached a system that forecasts air temperature using machine learning algorithms. Several regression methods were employed to attempt to predict temperatures. This research evaluated four algorithms (Decision Tree, AdaBoost, Random Forest, and Gradient Boosting) on some meteorological data over three years (2015-2019), where 80 percent of the total data set was utilized for training and tested on 20 percent. The variables used include Wind speed, Relative Humidity, Dew point, and Air pressure. The objective was to determine which regressor achieves better outcomes for forecasting air temperature with the lowest error rate. This research concluded that the Random Forest Regressor is the most accurate in prediction. Here, MAE is used to determine the accuracy. On average, the Random forest had the lowest MAE value of 0.102, which was lower than the outcomes of the other three algorithms.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134326215","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

IFPS-RPA: Interface-based Frequent Pattern Segmentation for Robotic Process Automation 基于接口的机器人过程自动化频繁模式分割

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217426

Aya Taha, Sherif A. Mazen, Iman M. A. Helal

引用次数: 0

Early Rheumatoid Arthritis Detection by miRNA Data Analysis Using a Hybrid CNN-LSTM Deep Learning Model 基于CNN-LSTM混合深度学习模型的miRNA数据分析早期类风湿关节炎检测

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217733

Nehal M. Ali

{"title":"Early Rheumatoid Arthritis Detection by miRNA Data Analysis Using a Hybrid CNN-LSTM Deep Learning Model","authors":"Nehal M. Ali","doi":"10.1109/IMSA58542.2023.10217733","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217733","url":null,"abstract":"Transcriptomic data analysis has significantly evolved thanks to the high throughput data technology. Analyzing this data can notably support the early detection of several diseases. At the same time, Rheumatoid arthritis (RA) is an autoimmune disease that causes critical joints damages over time and can lead to severe disabilities. This work introduces a hybrid CNN-LSTM deep learning model that combines the CNN capabilities of higher-level feature extractions and the LSTM efficiency of determining the long-term consequences between the transcriptomic terms. This model analyzes miRNA data of Rheumatoid Arthritis patients and healthy controls to provide an early detection model for this disease. In addition, this work studies the impact of NEBNEXT and NEXTFLEX sample preparation kits on the accuracy scores of the miRNA samples' classification. The studied dataset consists of 42 miRNA files of RA cases, healthy controls, and synthetic samples. The results of the proposed model were promising, denoting sensitivity, specificity, precision, accuracy, and F1 Score values of (0.875, 0.884, 0.88, 0.883, 0.872), respectively. Moreover, comparative experiments are conducted with literature work.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124677039","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

VARSew Benchmarking: A Visual Action Recognition on Garment Sewing using Various Deep learning models VARSew基准:使用各种深度学习模型的服装缝纫视觉动作识别

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217521

Aly Fathy, Ahmed Asaad, Ammar Mohammed

{"title":"VARSew Benchmarking: A Visual Action Recognition on Garment Sewing using Various Deep learning models","authors":"Aly Fathy, Ahmed Asaad, Ammar Mohammed","doi":"10.1109/IMSA58542.2023.10217521","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217521","url":null,"abstract":"The method of analyzing human action using computer and machine vision technologies is known as human action recognition (HAR). In recent years, research in this field has made substantial strides in a variety of application domains. However, HAR is still constrained in a number of fields because of the lack of sufficient, sizable training datasets. Many factors impact the availability of suitable HAR data in specific domains. Garment sewing is one of those interesting industrial fields. To demonstrate the growth of the global garment sewing business, several international reports were made public. As far as we know, HAR data for the garment sewing industry is limited. Thus, in this work, we benchmark and validate VARSew, a new dataset covering human garment sewing actions. VARSew is designed to capture multiple research challenges in HAR. We benchmark VARSew on two types of video classification tasks: binary and multi-class. VARSew contains 3,121 video segments of 49,936 frames. We conducted extensive benchmark experiments with 32 state-of-the-art HAR models and compare their performance. The results on both binary and multi-class reveal that the best accuracy scores are 80% and 95% for binary and multi-class classification, respectively. As a result, deep learning vision transformer (Vit) models outperformed deep learning convolutional and ResNet models on both the VARSew multi-class and VARSew binary-class datasets.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130133270","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

Anti-noise Star Image Extraction Algorithm for Star Trackers Based on YOLOv5 基于YOLOv5的星跟踪器抗噪星图提取算法

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217394

Feng Wu, Qingquan Xu, Ruxi Xiang, Quanying Wu, Xingzhi Wu, Xifang Zhu, Chen Wang, Rui Sun

{"title":"Anti-noise Star Image Extraction Algorithm for Star Trackers Based on YOLOv5","authors":"Feng Wu, Qingquan Xu, Ruxi Xiang, Quanying Wu, Xingzhi Wu, Xifang Zhu, Chen Wang, Rui Sun","doi":"10.1109/IMSA58542.2023.10217394","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217394","url":null,"abstract":"The spacecraft depend on accurate attitude measurement greatly. Star trackers are one of the most accurate attitude measurement devices which can provide the attitude data as low as arc-seconds. Star image extraction is to process the star maps obtained by imaging stars in the sky and calculate the positions of star images. Its performances determine whether star trackers can measure the attitudes successfully. The noises in space degrade the star imaging of star trackers severely and bring more difficulties to star image extraction. In this paper, in order to withstand the noise interference, an anti-noise star image extraction algorithm based on YOLOv5 for star trackers was proposed which recognized the star images directly from the noisy star maps and lessened the difficulty of the star image extraction. Firstly, the working principle of star trackers was introduced. The star image extraction procedures were analyzed. The anti-noise star image extraction algorithm was proposed after the structure of YOLOv5 was introduced. Its procedures were described in detail. Secondly, the training and testing data sets were created by applying the developed star imaging simulation method to produce star images disturbed by Gaussian noise and Poisson noise. Then experiments were fulfilled. It proved that the proposed algorithm achieved a satisfactory recognition of the star images even with strong noises.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130485061","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

TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forecasting TCN-MLP:短期电力负荷预测的混合模型

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217765

Karim S. Farid, A. A. Ali, Sameh A. Salem, Amr E. Mohamed

引用次数: 1

Classification of tumors based on distinguishing possibilistic biclusters 基于区分可能性双聚类的肿瘤分类

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217488

M. Mahfouz

{"title":"Classification of tumors based on distinguishing possibilistic biclusters","authors":"M. Mahfouz","doi":"10.1109/IMSA58542.2023.10217488","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217488","url":null,"abstract":"The application of machine learning techniques to analyze very high dimensional gene expression data in order to uncover the underlying biological mechanisms of complex diseases is one of the most crucial bioinformatics tasks. The focus of this research is the development of a multi-class classifier for the classification of tumors and identifying relevant genes based on distinguishing fuzzy biclusters. The proposed technique starts by computing k overlapping clusters of genes. For each cluster of genes C, we have c biclusters, where c is the number of classes, such that, for class t, a bicluster Bt = (C, classt samples). The clustering technique uses the average mean square residue (MSR) such that for each class t the MSR(Bt) is minimized while simultaneously maximizing the cluster size. Then a possibilistic technique is applied to each cluster to compute a membership for each gene in its cluster such that the assigned memberships maximize the weighted average of the fuzzy residue of each row in Bt` when added to Bt while minimize the number of genes and the weighted average of the fuzzy residue of each row in Bt in its bicluster by calculating the derivative’s zeros for the proposed objective function. The vector of possibilistic memberships associated with each cluster Ct works as a multi-class classifier. The decision space can be made more discriminative by combining the outcomes predicted by k separate models using stacking or weighted majority voting. In order to classify an unseen sample xu, the relative increase in the weighted average of the squared fuzzy residue of Bt, for each class t, when xu is added to it, is computed. Then, xu is assigned to a class t if the relative increase in the weighted average of the squared fuzzy residue of Bt is the minimum. Results from experiments indicate that the performance of the suggested technique and its computational complexity are comparable to algorithms that combine metaheuristic algorithms with SVM.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"68 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120858474","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

AI Personal Trainer for Lateral Raises and Shoulder Presses Exercises AI私人教练横向提升和肩部按压练习

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217601

Shehab Eldeen Hesham Mahmoud, Zeinab Abd El Haliem Taha

{"title":"AI Personal Trainer for Lateral Raises and Shoulder Presses Exercises","authors":"Shehab Eldeen Hesham Mahmoud, Zeinab Abd El Haliem Taha","doi":"10.1109/IMSA58542.2023.10217601","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217601","url":null,"abstract":"Resistance training has picked up popularity due to its various benefits, including improvements in muscle quality, control, balance, and coordination. In any case, this sort of training also postures certain risks, such as unexpected injuries to the back, muscles, ligaments, and tendons. This paper introduces an AI personal trainer that utilizes a webcam or mobile camera to guide users in performing shoulder exercises, such as lateral raises and shoulder presses, correctly. The AI trainer tracks repetitions, classifies exercises, calculates exercise speed, and displays joint coordinates graph using the $1 Unistroke Recognizer to demonstrate the user's range of motion after each session, allowing for comparison with a professional trainer’s graph. The AI trainer uses Mediapipe to extract 33 pose body coordinates and saves them to CSV files, resulting in a dataset of 81,937 samples. Two additional CSV files were generated for each exercise, resulting in a dataset of 16,496 samples, categorizing landmarks as 'up' or 'down' labels to detect hand movements and assist with repetition counting. The application offers real-time feedback and notifies the user when to slow down to avoid injury. Experimental results show that the KNN model achieved 100% accuracy for Exercise Type CSV, and the Random Forest Classifier achieved the highest accuracy of 99.80% and 98.49% for Lateral Raises and Shoulder Press Up and Down CSV datasets respectively. Consequently, these models were selected for implementation due to their high reliability in exercise classification.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120990391","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

DriveGuard: An Automated Interactive Alerting System to track and Analyze Drivers’ Behaviors on Road DriveGuard:一种自动交互式警报系统，用于跟踪和分析道路上驾驶员的行为

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217448

Abdallah Mohialdin, Dema Saleh, Elshaymaa Taher, Kareem Sabry, Hany M. El-Said, Ayman Atia

{"title":"DriveGuard: An Automated Interactive Alerting System to track and Analyze Drivers’ Behaviors on Road","authors":"Abdallah Mohialdin, Dema Saleh, Elshaymaa Taher, Kareem Sabry, Hany M. El-Said, Ayman Atia","doi":"10.1109/IMSA58542.2023.10217448","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217448","url":null,"abstract":"The increasing number of road accidents globally has highlighted the need for advanced safety measures in vehicular transportation. Driver alerting systems have emerged as a critical solution to reduce the risks associated with human error, fatigue, and distraction. These systems employ a combination of computer vision techniques, and machine learning algorithms to monitor driver behavior and vehicle performance in real-time. Driver alerting systems can mitigate hazardous situations such as sudden lane departures, inadequate mirror checks, and drowsy driving by providing timely alerts and interventions.This paper discusses the importance of driver alerting systems in enhancing road safety, improving driver performance, and reducing accident rates. It delves into the various technologies and methodologies utilized in the development of these systems, along with their potential integration with emerging connected and autonomous vehicle technologies. Throughout this paper, it is shown how using the application helped 75% of people have more focus while driving, this conclusion came from a conducted survey on people before and after using the application. The system also scored 81 point on average for the System Usability Scale (SUS) usability test. Ultimately, the widespread adoption of driver alerting systems is paramount to creating a safer and more sustainable transportation ecosystem.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124481266","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