2023 28th International Computer Conference, Computer Society of Iran (CSICC)最新文献_第3页

A Hybrid Deep Learning Network for Sentiment Analysis on SemEval-2017 Dataset 基于SemEval-2017数据集的情感分析混合深度学习网络

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105312

Bahar Sar-Saifee, J. Tanha, Mohammad Aeini

{"title":"A Hybrid Deep Learning Network for Sentiment Analysis on SemEval-2017 Dataset","authors":"Bahar Sar-Saifee, J. Tanha, Mohammad Aeini","doi":"10.1109/CSICC58665.2023.10105312","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105312","url":null,"abstract":"Nowadays, the vast amount of information on the internet enables people to be informed of other thoughts and ideas quickly and get an insight to use in their decisions. Knowing how people feel about a person or event can significantly impact the decisions of individuals and organizations. With the advent of social networks and their high popularity, most people tend to share their opinions on the internet. Analyzing these feelings on social networks, as a good representation of society, can help make organizational decisions and forecast important events. Therefore, processing large volumes of information is a challenge that many researchers have considered. This study aims to present a new approach to analyzing emotions and detecting the polarity of the opinions of Twitter social network users using deep learning algorithms. The use of deep learning networks on textual data requires pre-processing and text conversion into vector space, so textual data will be transformed into vector space using the word embedding structure. This study analyzes the Twitter social network due to its data availability and significant application in emotion classification and uses the SemEval-2017 Task 4 dataset. We propose a hybrid model of the LSTM, CNN, and GRU networks, using CNN to extract the text features and the LSTM and GRU networks to preserve long-term dependencies. Moreover, we handle an imbalanced dataset using data augmentation techniques. Then we evaluate the performance of the proposed model using multiple metrics. The results show that our proposed method is about 10% better than previous related works.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122917885","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 Noval Reduced Particle Swarm Optimization With Improved Learning Strategy and Crossover Operator 基于改进学习策略和交叉算子的简化粒子群算法

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105402

M. Nazari, M. Esnaashari, Mohammadreza Parvizimosaed, A. Damia

引用次数: 0

Multi-Objective Optimization for Neural Network Structure 神经网络结构的多目标优化

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105405

M. Shokoohi, M. Teshnehlab

{"title":"Multi-Objective Optimization for Neural Network Structure","authors":"M. Shokoohi, M. Teshnehlab","doi":"10.1109/CSICC58665.2023.10105405","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105405","url":null,"abstract":"This study presents a new algorithm for training flexible perceptron multilayer neural networks. This algorithm is based on the multi-objective evolutionary optimization and tries to find the smallest optimal structure simultaneously by reducing the network error. In this method, a compatibility is established between the mean squared error and the vector length of the parameters of the activation functions by using flexible neurons which cause a greater degree of freedom leading to a faster convergence of the neural network. Then, the network structure decreases and the problem of overfitting and local minimum is prevented based on the values of these parameters and the use of the integration method of neurons. Moreover, it increases the power of the generalizability of the neural network. This method was used for classification problems, and the results were compared with AMGA, BCPA, LASSO, and Early Stopping methods. Based on the results, the algorithm proposed in this study usually works better compared to the similar algorithms. In addition, the proposed algorithm is a systematic method for finding the optimal neural network structure.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115302631","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

Predicting the Load Capacity of 4G Cellular Networks With Deep Learning 基于深度学习的4G蜂窝网络负载能力预测

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105423

H. Azadegan, Farzaneh Esmaili

引用次数: 0

An election strategy for online streaming feature selection 一种在线流媒体特征选择的选举策略

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105319

Amin Hashemi, Mohammad-Reza Pajoohan, M. B. Dowlatshahi

引用次数: 0

A drug recommender system Based on Collaborative Filtering for Covid-19 patients 基于协同过滤的Covid-19患者药物推荐系统

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105347

Narges Mohebbi, Meysam Alavi, M. Kargari, Seyed Hamidreza Mirbehbahani, Amir Behnam Kharazmy

{"title":"A drug recommender system Based on Collaborative Filtering for Covid-19 patients","authors":"Narges Mohebbi, Meysam Alavi, M. Kargari, Seyed Hamidreza Mirbehbahani, Amir Behnam Kharazmy","doi":"10.1109/CSICC58665.2023.10105347","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105347","url":null,"abstract":"The epidemic caused by a new mutation of the coronavirus family called Covid-19 has created a global crisis involving all the world's countries. This disease has become a severe danger to everyone due to its unknown nature, high spread, and inability to detect the infected. In this regard, one of the important issues facing patients with Covid-19 is the prescription of Drugs according to the severity of the disease and considering the records of underlying diseases in people. In recent years, recommender systems have been developed significantly along with the advancement in information technology and artificial intelligence, which is one of its applications in various fields of medical sciences. Among them, we can refer to recommending systems for the prevention, control, and treatment of diseases. In this research, using the collaborative filtering approach as one of the types of recommender systems as well as the K-means clustering algorithm, a Drug recommendation system for patients with Covid-19 in the treatment stage of the disease is presented. The results of this research show that this recommender system has an acceptable performance based on the evaluation criteria of precision, recall, and F1-score compared to the opinions of experts in this field.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126625210","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

SBSN: Harvesting Stable Body Sensor Node by Providing an Energy Efficient Adaptive Sampling Method 基于能量高效自适应采样方法的稳定身体传感器节点采集

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105340

Razieh Mohammadi, Z. Shirmohammadi

{"title":"SBSN: Harvesting Stable Body Sensor Node by Providing an Energy Efficient Adaptive Sampling Method","authors":"Razieh Mohammadi, Z. Shirmohammadi","doi":"10.1109/CSICC58665.2023.10105340","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105340","url":null,"abstract":"Wireless Body Area Networks (WBANs) are an efficient solution to monitor the vital signs of patients. In WBAN, the sensors' operation stops as soon as the battery is discharged. Hence, it is expected that sensors can continue their operations stably to provide stable services. The problem of sensors' stability can solves by using energy harvesters and providing unlimited energy, but the main challenge is that the energy harvested at different times has different rates. Therefore, energy-saving methods should be taken into consideration along with energy-harvesting techniques. The sampling operation has the highest energy consumption in WBAN. Adaptive sampling methods conserve energy to a high degree. This paper proposes a new method for combining energy harvesting technique and adaptive sampling to create a stable body sensor node (SBSN). In SBSN, sensors are classified into three classes based on their energy levels. The rate of sampling in each class is determined according to various methods considering energy levels to ensure the self-stability of the sensors. The simulations show that, compared to the state-of-the-art methods, the SBSN can reduce the data overhead by 73% on average while conserving data integrity. In addition, SBSN makes the sensor self-stable and keeps the energy level of the sensors up to 2.9 times higher on average.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125553287","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

Identification Important Nodes in Diffusion Process by User Experience on Social Network 利用用户体验识别社交网络扩散过程中的重要节点

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105397

F. Kazemzadeh, A. Safaei, M. Mirzarezaee

{"title":"Identification Important Nodes in Diffusion Process by User Experience on Social Network","authors":"F. Kazemzadeh, A. Safaei, M. Mirzarezaee","doi":"10.1109/CSICC58665.2023.10105397","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105397","url":null,"abstract":"The influence maximization problem (IMP) has been proposed in social networks. Nowadays, it is considered an important and practical problem due to the earnings potential by identifying a set of influential nodes, and therefore, it has been attracted by many researchers. This problem seeks to identify a set with K nodes among the social network nodes to maximize the influence and diffusion of information in that community. Algorithms proposed by other researchers have many shortcomings in terms of accuracy and run time of the algorithm. Hence, this article aimed to find the best, most accurate, and fastest solution to the problem.The article presented the UXM algorithm and used the User Experience criterion for the first time to solve this problem. At first, taking into account the reach club phenomenon and using criteria such as clustering coefficient, degree and also using user experience, nodes with more influence have been selected as the primary candidate set. Then, according to the component nodes, K final influential nodes have been selected. In this way, it could identify the set of nodes as accurately as possible with high efficiency in the shortest possible time. The evaluation of this algorithm and its comparison with other algorithms indicated excellent results in terms of run time and accuracy in selecting the set of nodes by the proposed algorithm.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122587114","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

Analyzing the Use of Auditory Filter Models for Making Interpretable Convolutional Neural Networks for Speaker Identification 分析使用听觉滤波模型制作可解释卷积神经网络用于说话人识别

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105387

Hossein Fayyazi, Y. Shekofteh

{"title":"Analyzing the Use of Auditory Filter Models for Making Interpretable Convolutional Neural Networks for Speaker Identification","authors":"Hossein Fayyazi, Y. Shekofteh","doi":"10.1109/CSICC58665.2023.10105387","DOIUrl":"https://doi.org/10.1109/CSICC58665.2023.10105387","url":null,"abstract":"Most of the advances in artificial intelligence are based on understanding the function of different parts of the living organism's body. High complexity of some body parts, e.g., the brain, leads to using some abstractions for making intelligent models, which can make the models uninterpretable. This general process can be seen in the development of Deep Neural Networks (DNNs). Although DNNs are models with high performance, they have a black-box nature which makes them unreliable in some applications such as medicine. Fortunately, nature can again help to make DNN models explainable. The use of meaningful filters in the first layer of Convolutional Neural Networks (CNNs) is a successful attempt in this field. The goal of this paper is to examine the use of some auditory filter models as CNN front-ends to make them interpretable and then to evaluate the resulting filter banks in the Speaker Identification (SID) task. Results confirm the previous knowledge about the filtering mechanism of the auditory system. This simple observation can lead to an abstract conclusion that making a complex learning model interpretable, specifically using simple elements inspired by nature, can disclose the hidden aspects of how the human body works. Moreover, replicating the essential functions of the human auditory system leads to better model performance.","PeriodicalId":127277,"journal":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116038150","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

OutCLIP, A New Multi-Outfit CLIP Based Triplet Network OutCLIP，一种新的基于多装备CLIP的三重网络

2023 28th International Computer Conference, Computer Society of Iran (CSICC) Pub Date : 2023-01-25 DOI: 10.1109/CSICC58665.2023.10105384

Zahra Haghgu, R. Azmi, Lachin Zamani, Fatemeh Moradian

引用次数: 0