2023 9th International Conference on Web Research (ICWR)最新文献

A Novel CNN-LSTM Model for Predicting the Railway Sector Stock Price in the Tehran Stock Exchange 德黑兰证券交易所铁路板块股票价格预测的CNN-LSTM新模型

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139258

Faraz Bodaghi, Amin Owhadi, Arash Khalili Nasr, Melody Khadem Sameni

{"title":"A Novel CNN-LSTM Model for Predicting the Railway Sector Stock Price in the Tehran Stock Exchange","authors":"Faraz Bodaghi, Amin Owhadi, Arash Khalili Nasr, Melody Khadem Sameni","doi":"10.1109/ICWR57742.2023.10139258","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10139258","url":null,"abstract":"As a country's financing arm, the capital market can play a crucial role in long-term projects and be responsible for large-scale financing. While the development of rail transport has exceptionally positive and significant effects on the country's macroeconomy, and investment in this sector leads to the prosperity of other economic sectors, the return on investment may be delayed or unclear. Consequently, this uncertainty diminishes the investment appeal in this field. Thus, predicting the price and future value of railway company stocks on the stock market is vital due to the need for the foresight to take safe investment measures and achieve sustainable financing. To this end, this paper investigated the use of deep learning methods to forecast the closing price of MAPNA and Toucaril stocks on the Tehran Stock Exchange. Stock prices were predicted using deep neural networks, including Long Short-Term Memory (LSTM), One-dimensional Convolutional Neural Networks (1D-CNN), and CNN-LSTM networks as a hybrid model. Finally, to evaluate the performance of the models, MAE, MSE, RMSE, MAPE, and R2 were used as evaluation criteria. The results indicate that deep learning models can forecast stock prices with accuracy. In this study, CNN-LSTM neural network produced the best results.","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"24 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121008170","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

Real-Time Blood Pressure Prediction Using Apache Spark and Kafka Machine Learning 使用Apache Spark和Kafka机器学习进行实时血压预测

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10138962

Ali Farki, Elham Akhond Zadeh Noughabi

{"title":"Real-Time Blood Pressure Prediction Using Apache Spark and Kafka Machine Learning","authors":"Ali Farki, Elham Akhond Zadeh Noughabi","doi":"10.1109/ICWR57742.2023.10138962","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10138962","url":null,"abstract":"Using a mix of machine learning algorithms and big data tools, particularly Apache Spark and also Apache Kafka, this research provides a new method for real-time blood pressure prediction. The method can handle large amounts of inbound data from numerous sources, including wearable technology and internet of things monitors. A clustering-based approach is used to improve the blood pressure estimation's precision while the data is being analyzed in real-time. ECG, PPG, and ABP signals dataset are used to assess the suggested strategy, and the findings show a substantial improvement in blood pressure prediction accuracy when compared to previous methods. The suggested method has the potential to be used in numerous uses, such as remote patient tracking, individualized healthcare, and cardiovascular disease early detection. This research offers two contributions. First off, it introduces a novel technique for real-time blood pressure forecast that is more accurate than current approaches. In addition, it shows the value of merging machine learning techniques with real-time streaming data processing systems like Apache Spark and Apache Kafka. Further improving the scalability and accuracy of the system is the use of web-based tools and deep learning methods. The suggested method may have a big impact on how well patients do and how much it will cost to treat them. Overall, this research offers a path that can be useful to both individuals and healthcare professionals for the creation of real-time blood pressure forecast tools.","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117088529","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

Evaluating Security Anomalies by Classifying Traffic Using Deep Learning 基于深度学习的流量分类安全异常评估

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10138963

M. Samadzadeh, Najmeh Farajipour Ghohroud

{"title":"Evaluating Security Anomalies by Classifying Traffic Using Deep Learning","authors":"M. Samadzadeh, Najmeh Farajipour Ghohroud","doi":"10.1109/ICWR57742.2023.10138963","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10138963","url":null,"abstract":"Network traffic identification is an essential function for network domain systems, which facilitates accurate management through the classification of network traffic flows. In this research we used traffic separation using deep learning approach to detect security anomalies. The method proposed has several steps. Since many features are usually used to detect network security anomalies, in the first stage, feature selection was an optional step to select some of the most important features associated with the problem of detecting security anomalies in the network. Then the SMOTE balancing method was used to balance the data when the evaluated data set was unbalanced in class distribution. The results of balanced data and imbalanced data were obtained. Ultimately, the convolutional neural network was used to train the proposed model. The proposed model was tested and evaluated after training the model. The evaluation results indicated that in the mode of feature reduction and data balancing, the proposed CNN classifier showed the accuracy of 96.88% and 98.18% in feature reduction and data imbalance mode, when using no feature reduction and data balancing mode we reached the accuracy of 97.35% and 98.57% accuracy in feature reduction and non-balancing data.","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"56 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129928973","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

Preventing the Elderly with Diabetes from the Risk of Falling using a Multilayer Perception Classifier 多层感知分类器预防老年糖尿病患者跌倒风险

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139274

Zahra Alidousti Shahraki, Mohsen Aghabozorgi Nafchi

{"title":"Preventing the Elderly with Diabetes from the Risk of Falling using a Multilayer Perception Classifier","authors":"Zahra Alidousti Shahraki, Mohsen Aghabozorgi Nafchi","doi":"10.1109/ICWR57742.2023.10139274","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10139274","url":null,"abstract":"Elderly people are an important age group in society that must be constantly under control. Because of old age, these people suffer from various diseases such as blood sugar, blood pressure, and obesity. If the elderly are not treated and their diseases are not controlled by taking medicine, it will cause headaches, dizziness, and blurred vision, which will cause problems for them as a result of these symptoms. One of the problems they cause is physical and mental injury. Physical injuries in the elderly population are caused by falls. Therefore, providing methods to prevent them from falling can play an important role in their physical and mental health as well as in improving their quality of life. Machine learning algorithms can predict the condition of the elderly at any moment. If an elderly person has acute symptoms, the risk of falling can be prevented by turning on the smart sensors. In this study, we propose a method for predicting falls in elderly individuals with diabetes using a multilayer perception classifier. According to these results, elderly people with diabetes are more prone to falls because of symptoms caused by blood sugar, such as dizziness and motor nerve disorders. By designing an intelligent system that can predict the condition of the elderly based on various diseases, it is possible to prevent them from falling. In this study, various factors collected from this dataset were examined. By reviewing the data and confirming the opinion of a cardiovascular disease specialist, the elderly with diabetes had more symptoms, such as headache, dizziness, and blurred vision, than the rest of the elderly; therefore, these symptoms lead to a drop in blood pressure and fall. The results obtained from the implementation of the machine learning algorithm showed that machine learning could predict the risk of falling in elderly patients with diabetes with a high performance (accuracy=97%).","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129102728","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

Re-rank Search Engine Results via Machine Learning 通过机器学习重新排列搜索引擎结果

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139285

Amir Hussein Ataei Monfared, Amir Hossein Avizheh

{"title":"Re-rank Search Engine Results via Machine Learning","authors":"Amir Hussein Ataei Monfared, Amir Hossein Avizheh","doi":"10.1109/ICWR57742.2023.10139285","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10139285","url":null,"abstract":"Research on search engines has demonstrated their significant impact on information search and user behavior and highlighted the need for continued investigation of their effectiveness, efficiency, and ethical implications. Moreover, search engine optimization (SEO) has emerged as a crucial aspect of online content creation and marketing, as it involves optimizing websites and online materials to improve their visibility and ranking on search engine results pages, thereby increasing their reach and impact. SEO metrics provide valuable insight into a website’s search engine performance. However, the relative importance of these metrics remains unclear as search engine algorithms and keyword competitiveness can vary widely. Therefore, ongoing research is needed to better understand the relationship between SEO metrics and search engine rankings. Given the challenge of determining the relative importance of SEO metrics using traditional statistical methods, interested in applying machine learning techniques. By training models with custom datasets of three search engine results (Google, Bing, and DuckDuckGo) and their associated SEO metrics, we hope to gain a better understanding of the complex relationships between these variables. The first two pages of each search engine return the most relevant results. In this article, we propose two types of methods to do this. First, classify the page index of web search results. Second, learning ranking methods to rank them. LambdaRank has the best NDCG compared to other methods for Google and DuckDuckGo which are 0.86 and 0.93 respectively. RankNet is the best learning-to-rank method for Bing with 0.85 as the NDCG value. Logistic regression has the highest accuracy compared to other methods in page index classification for Bing and Google, which are 61.29% and 64.71%, respectively. K-nearest neighbor performs best for DuckDuckGo with an accuracy of 66.67%.","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115770406","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

IoMT-based Automated Leukemia Classification using CNN and Higher Order Singular Value Decomposition 基于iom的基于CNN和高阶奇异值分解的白血病自动分类

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139301

Shabnam Bagheri Marzijarani, Mohammad Zolfaghari, H. Sajedi

{"title":"IoMT-based Automated Leukemia Classification using CNN and Higher Order Singular Value Decomposition","authors":"Shabnam Bagheri Marzijarani, Mohammad Zolfaghari, H. Sajedi","doi":"10.1109/ICWR57742.2023.10139301","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10139301","url":null,"abstract":"The Internet of Things (IoT) is a concept by which objects find identity and can communicate with each other in a network. One of the applications of the IoT is in the field of medicine, which is called the Internet of Medical Things (IoMT). Acute Lymphocytic Leukemia (ALL) is a type of cancer categorized as a hematic disease. It usually begins in the bone marrow due to the overproduction of immature White Blood Cells (WBCs or leukocytes). Since it has a high rate of spread to other body organs, it is a fatal disease if not diagnosed and treated early. Therefore, for identifying cancerous (ALL) cells in medical diagnostic laboratories, blood, as well as bone marrow smears, are taken by pathologists. However, manual examinations face limitations due to human error risk and time-consuming procedures. So, to tackle the mentioned issues, methods based on Artificial Intelligence (AI), capable of identifying cancer from non-cancer tissue, seem vital. Deep Neural Networks (DNNs) are the most efficient machine learning (ML) methods. These techniques employ multiple layers to extract higher-level features from the raw input. In this paper, a Convolutional Neural Network (CNN) is applied along with a new type of classifier, Higher Order Singular Value Decomposition (HOSVD), to categorize ALL and normal (healthy) cells from microscopic blood images. We employed the model on IoMT structure to identify leukemia quickly and safely. With the help of this new leukemia classification framework, patients and clinicians can have realtime communication. The model was implemented on the Acute Lymphoblastic Leukemia Image Database (ALL-IDB2) and achieved an average accuracy of %98.88 in the test step.","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228029","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

Providing a Solution for Optimal Management of Resources using the Multi-objective Crow Search Algorithm in Cloud Data Centers 基于多目标乌鸦搜索算法的云数据中心资源优化管理解决方案

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139192

Nastaran Evaznia, R. Ebrahimi

{"title":"Providing a Solution for Optimal Management of Resources using the Multi-objective Crow Search Algorithm in Cloud Data Centers","authors":"Nastaran Evaznia, R. Ebrahimi","doi":"10.1109/ICWR57742.2023.10139192","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10139192","url":null,"abstract":"Cloud data centers are a model of distributed systems that provide users with services to share data and information through the Internet. These centers also face challenges due to their popularity among users. With the increase in the number of hosts to respond to users' needs, challenges such as increasing power consumption, service level agreement violation, and time are seen. As a result, it is very important to address these challenges in these centers in order to reduce costs and increase profits. task scheduling for hosts is one of the most effective methods to improve productivity and optimal use of hosts' resources. In this process, with proper allocation, we can prevent hosts from becoming overloaded and increasing energy consumption due to inefficient use of hosts' resources. The proposed solution in this paper is to use multiple goals for the allocation process using the Crow search optimization algorithm. The Crow search optimization algorithm is new, fast, and powerful. As a result, in the proposed method by modeling this algorithm and considering the multi-criteria fitness function based on the requested resources of the tasks and the available resources of the host, we tend to manage resources properly. The simulation results show that the proposed method has a 9% reduction in service quality parameters such as power consumption compared to paper [21] and 15% compared to article [16], 11% execution time compared to paper [21], and 14% compared to the paper [16] and the service level agreement violation has improved by 16% compared to the paper [21] and 8% compared to the paper [16] and has been able to reduce the mentioned parameters.","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115744865","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

Persian Ezafeh Recognition using Transformer-Based Models 基于变压器模型的波斯Ezafeh识别

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139204

A. Ansari, Zahra Ebrahimian, Ramin Toosi, M. Akhaee

引用次数: 0

Supervised Machine Learning Models for Multiple Sclerosis Relapse Probability Prognosis using Clinical Data 基于临床数据的多发性硬化症复发概率预测的监督机器学习模型

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139225

Fatemeh Vakili, Zahra Vakili, M. Kargari, Mehran Ghaffari

{"title":"Supervised Machine Learning Models for Multiple Sclerosis Relapse Probability Prognosis using Clinical Data","authors":"Fatemeh Vakili, Zahra Vakili, M. Kargari, Mehran Ghaffari","doi":"10.1109/ICWR57742.2023.10139225","DOIUrl":"https://doi.org/10.1109/ICWR57742.2023.10139225","url":null,"abstract":"Multiple sclerosis (MS) is an autoimmune disease that causes physical disabilities over time. The prevalence of this disease is increasing in the world. Since this disease causes physical disabilities, the prognosis of relapse is vital. Currently, doctors use magnetic resonance images to diagnose the disease and its process. But this method suffers from some problems such as operator fatigue, the difficulty of using magnetic resonance images, the complications of its use, etc. For this reason, machine learning techniques can be used as diagnostic aids for doctors. Some data were from the hospital’s HIS and electronic health records. Some other physical files of patients have been used. Overall, 492 records of 84 patients with relapsing-remitting type were collected. We studied the clinical data of patients with disease onset for two years. The data were collected from the MS clinic of Imam Hossein Hospital in Tehran. The patient's clinical symptoms were recorded from February 2021 to February 2023. Mainly, the patient's symptoms were classified into 7 groups of relapses. Also, the patient’s personal information and relapse symptoms were examined. With the help of machine learning techniques, we will design a model that predicts the probability of relapse in the next year. The data were unbalanced, so the SMOTE oversampling method has been used. The results show that these models predicted relapse in patients with high accuracy and precision. This research used standard machine learning models SVC, K-NN, Logistic Regression, Decision Tree, Random Forest, Naive Bayes, MLP, XGBoost, Ada Boost, and Gradient Boost. The results of evaluating the models based on accuracy and precision criteria showed that the SVC with 98.837% precision and 97.973% accuracy and followed by 97.674% precision and 97.297% accuracy in both LogisticRegression and MLP models are the best model and can provide an appropriate prognosis.","PeriodicalId":146989,"journal":{"name":"2023 9th International Conference on Web Research (ICWR)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124709356","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

A Persian Wake Word Detection System Based on the Fine Tuning of A Universal Phone Decoder and Levenshtein Distance 基于通用电话解码器和Levenshtein距离微调的波斯语唤醒词检测系统

2023 9th International Conference on Web Research (ICWR) Pub Date : 2023-05-03 DOI: 10.1109/ICWR57742.2023.10139277

Dorreen Rostami, Y. Shekofteh

引用次数: 0