检索结果-内蒙古大学图书馆

IEEE Symposium on Systems and Information engineering Design, SIEDS

作者： Aram Bahrini Mohammadsadra Khamoshifar Hossein Abbasimehr Robert J. Riggs Maryam Esmaeili Rastin Mastali Majdabadkohne Morteza Pasehvar Department of Systems and Information Engineering University of Virginia Charlottesville VA USA School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Industrial Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Medicine Faculty of Medical Sciences The American University Managua Nicaragua Department of Industrial Engineering Faculty of Engineering Islamic Azad University Tehran Iran

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A generalizable approach based on U-Net model for automatic Intra retinal cyst segmentation in SD-OCT images

arXiv

引用

arXiv 2022年

作者： Ganjee, Razieh Moghaddam, Mohsen Ebrahimi Nourinia, Ramin The Faculty of Computer Science and Engineering Shahid Beheshti University G.C Tehran Iran Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran

Intra retinal fluids or Cysts are one of the important symptoms of macular pathologies that are efficiently visualized in OCT images. Automatic segmentation of these abnormalities has widely investigated in medical image processing studies. In this paper, we propose a new U-Net-based approach for Intra retinal cyst segmentation across different vendors that improves some of the challenges faced by previous deep-based techniques. The proposed method has two main steps: 1- prior information embedding and input data adjustment, and 2- IRC segmentation model. In the first step, we inject the information into the network in a way that overcomes some of the network limitations in receiving data and learning important contextual knowledge. And in the next step, we introduced a connection module between encoder and decoder parts of the standard U-Net architecture that transfers information more effectively from encoder to decoder part. Two public datasets namely OPTIMA and KERMANY were employed to evaluate the proposed method. Results showed that the proposed method is an efficient vendor independent approach for IRC segmentation with mean Dice values of 0.78 and 0.81 on the OPTIMA and KERMANY datasets, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation

arXiv

引用

arXiv 2025年

作者： Aghaei, Raha Kiaei, Ali A. Boush, Mahnaz Vahidi, Javad Barzegar, Zeynab Rofoosheh, Mahan School of Mathematics and Computer Science Iran University of Science & Technology Tehran Iran Department of Computer engineering Sharif University of Technology Tehran Iran Department of Artificial Intelligence in Medicine Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran Cellular and Molecular Biology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Computer engineering group Alborz Vocational Technical University Alborz Iran

The integration of large language models (LLMs) into supply chain management (SCM) is revolutionizing the industry by improving decision-making, predictive analytics, and operational efficiency. This white paper explores the transformative impact of LLMs on various SCM functions, including demand forecasting, inventory management, supplier relationship management, and logistics optimization. By leveraging advanced data analytics and real-time insights, LLMs enable organizations to optimize resources, reduce costs, and improve responsiveness to market changes. Key findings highlight the benefits of integrating LLMs with emerging technologies such as IoT, blockchain, and robotics, which together create smarter and more autonomous supply chains. Ethical considerations, including bias mitigation and data protection, are taken into account to ensure fair and transparent AI practices. In addition, the paper discusses the need to educate the workforce on how to manage new AI-driven processes and the long-term strategic benefits of adopting LLMs. Strategic recommendations for SCM professionals include investing in high-quality data management, promoting cross-functional collaboration, and aligning LLM initiatives with overall business goals. The findings highlight the potential of LLMs to drive innovation, sustainability, and competitive advantage in the ever-changing supply chain management landscape. Copyright © 2025, The Authors. All rights reserved.

关键词： Competition

来源：评论

学校读者我要写书评

暂无评论

Enhancing Web Page Classification Through a Semantic-Aware and Efficient Focused Crawling Methodology

Enhancing Web Page Classification Through a Semantic-Aware a...

引用

International Conference on Web Research (ICWR)

作者： Mojdeh Nazari Hossein Sadr Shayan Rostami Zeinab Khodaverdian Department of Health Information Technology and Management Shahid Beheshti University of Medical Sciences Tehran Iran Department of Artificial Intelligence in Medicine Faculty of Advanced Technologies in Medicine Sciences Tehran Iran Department of Computer Engineering Ayandegan Institute of Education Tonekabon Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Focused crawlers are designed to search the web for pages on specific topics, aiming to gather relevant, preprocessed pages while ignoring unrelated ones. However, traditional focused crawlers often struggle with accurately categorizing web pages across multiple topics. The challenge stems from the vast amount of unstructured data found on web pages, making it difficult to classify them based on a particular subject. This study seeks to improve focused crawling by leveraging web page classification techniques. We propose a text classification model that combines the GloVe word vector model with the TF-IDF weighting method to enhance classification accuracy. This GloVe-based model is then used to help focused crawlers categorize web pages more effectively. We validated the proposed approach on 10 datasets, comparing its performance with traditional machine learning algorithms and other methods like Naive Bayes, Bag-of-Words, and Word2Vec. The results show that our model outperforms traditional algorithms by $7-12 \%$ .

关键词： Measurement Machine learning algorithms Accuracy Computational modeling Text categorization Crawlers Web pages Machine learning Vectors Bayes methods

来源：评论

学校读者我要写书评

暂无评论

Towards Study of Research Topics Evolution in Artificial Intelligence based on Topic Embedding

Towards Study of Research Topics Evolution in Artificial Int...

引用

International eConference on computer and Knowledge engineering (ICCKE)

作者： Seyyed Reza Taher Harikandeh Sadegh Aliakbary Soroush Taheri Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781665402095

Artificial Intelligence (AI) is one of the hottest trending research topics of computer science. As it is a fast-growing domain, analyzing its changes during different periods will help researchers analyze its evolution in order to develop their career path according to possible changes in the future. In this paper, we investigate how AI subfields are becoming closer or further from each other. This research also offers a general methodology for studying the relationships and interactions of research topics over time. In this regard, we adopt a topic embedding approach to examine how various sub-fields of AI evolve over time. Topic embedding is a method to convert topics to meaningful mathematical models so that their latent relationships remain. Besides being computationally efficient, this method has proven the ability to reveal significant patterns regarding the relationships between sub-domains.

关键词： Knowledge engineering computer science Social networking (online) engineering profession Market research Motion pictures Robustness

来源：评论

学校读者我要写书评

暂无评论

Adversarial Weakly Supervised Domain Adaptation for Few Shot Sentiment Analysis

Adversarial Weakly Supervised Domain Adaptation for Few Shot...

引用

International Conference on Web Research (ICWR)

作者： Seyyed Ehsan Taher Mehrnoush Shamsfard Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

The ability of deep neural networks to generate state-of-the-art results on many NLP problems has been apparent to everyone for some years now. However, when there is not enough labeled data or the test dataset has domain shift, these networks face many challenges and results are getting *** this article, we present a method for adapting the domain from formal to colloquial (in sentiment classification). Our method uses two approaches, adversarial training and weak supervision, and only needs a few shots of labeled *** the first stage, we label a crawled dataset (containing colloquial and formal sentences) with weakly supervised sentiment tags using a sentiment vocabulary network. Then we fine-tune a pre-trained model with adversarial training on this weak dataset to generate domain-independent representations. In the last stage, we train the above fine-tuned neural network with just 50 samples of data (formal domain) and test it on *** results show that our method outperforms the state-of-the-art model (Pars BERT) on the same data with 15% higher F1 measure.

关键词： Training Vocabulary Sentiment analysis Adaptation models Neural networks Bit error rate Data models

来源：评论

学校读者我要写书评

暂无评论

Malfustection: Obfuscated malware detection and malware classification with data shortage by combining semi-supervised and contrastive learning

arXiv

引用

arXiv 2021年

作者： Maghouli, Mohammad Mahdi Fereydooni, Mohamadreza Abdoos, Monireh Vahidi-Asl, Mojtaba Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

With the advent of new technologies, using various formats of digital gadgets is becoming widespread. In today's world, where everyday tasks are inevitable without technology, this extensive use of computers paves the way for malicious activity. As a result, it is important to provide solutions to defend against these threats. Malware is one of the well-known and widely used means utilized for doing destructive activities by malicious attackers. Producing malware from scratch is somewhat difficult, so attackers tend to obfuscate existing malware and prepare it to become an unrecognizable program. Since creating new malware from an old one using obfuscation is a creative task, there are some drawbacks to identifying obfuscated malwares. In this research, we propose a solution to overcome this problem by converting the code to an image in the first step and then using a semi-supervised approach combined with contrastive learning. In this case, an obfuscation in the malware bytecode corresponds to an augmentation in the image. Hence, by utilizing meaningful augmentations, which simulate some obfuscation changes and combine them to generate complex ambiguity procedures, our proposed solution is able to construct, learn, and detect a wide range of obfuscations. This work addresses two issues: 1) malware classification despite the data deficiency and 2) obfuscated malware detection by training on non-obfuscated malwares. According to the results, the proposed method overcomes the data shortage problem in malware classification, as its accuracy is 90.1% when just 10% of data is used for training the model. Moreover, training on basic malwares without obfuscation achieved 96.21 percent accuracy in detecting obfuscated malware. © 2021, CC BY.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

Parkinson's disease diagnosis based on gait cycle analysis through an interpretable interval type-2 neuro-fuzzy system

arXiv

引用

arXiv 2021年

作者： Salimi-Badr, Armin Hashemi, Mohammad Saffari, Hamidreza Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

In this paper, an interpretable classifier using an interval type-2 fuzzy neural network for detecting patients suffering from Parkinson's Disease (PD) based on analyzing the gait cycle is presented. The proposed method utilizes clinical features extracted from the vertical Ground Reaction Force (vGRF), measured by 16 wearable sensors placed in the soles of subjects' shoes and learns interpretable fuzzy rules. Therefore, experts can verify the decision made by the proposed method based on investigating the firing strength of interpretable fuzzy rules. Moreover, experts can utilize the extracted fuzzy rules for patient diagnosing or adjust them based on their knowledge. To improve the robustness of the proposed method against uncertainty and noisy sensor measurements, Interval Type-2 Fuzzy Logic is applied. To learn fuzzy rules, two paradigms are proposed: 1- A batch learning approach based on clustering available samples is applied to extract initial fuzzy rules, 2- A complementary online learning is proposed to improve the rule base encountering new labeled samples. The performance of the method is evaluated for classifying patients and healthy subjects in different conditions including the presence of noise or observing new instances. Moreover, the performance of the model is compared to some previous supervised and unsupervised machine learning approaches. The final Accuracy, Precision, Recall, and F1 Score of the proposed method are 88.74%, 89.41%, 95.10%, and 92.16%. Finally, the extracted fuzzy sets for each feature are reported. © 2021, CC BY-NC-ND.

关键词： Fuzzy inference

来源：评论

学校读者我要写书评

暂无评论

Short-Term Traffic Flow Prediction Based on a Recurrent Deep Neural Network: a Study in Tehran

Short-Term Traffic Flow Prediction Based on a Recurrent Deep...

引用

Conference on Information and Knowledge Technology

作者： Monireh Abdoos Taha Vajedsamiei Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781665408509

With growing of population, the issue of optimal mobility between two points of the city has become one of the most important problems. There are various tools to suggest the optimal route, but due to the momentary changes in traffic in cities, especially large cities, providing the optimal route without predicting the traffic load will not be accurate. In this regard, it can be noted that one of the most widely used up-to-date methods is the use of deep neural networks to predict the future. In this paper, while examining some of the most widely used deep neural networks to predict traffic sequence, a method is presented based on one of recurrent neural networks. The method has been evaluated on real traffic data on a part of Tehran. The results show that the proposed method outperforms the other similar neural networks.

关键词： Deep learning Training Recurrent neural networks Urban areas Sociology Transportation Telecommunication traffic

来源：评论

学校读者我要写书评

暂无评论

A Genetic-based Fusion Approach of Persian and Universal Phonetic Results for Spoken Language Identification

A Genetic-based Fusion Approach of Persian and Universal Pho...

引用

International eConference on computer and Knowledge engineering (ICCKE)

作者： Ashkan Moradi Yasser Shekofteh Saeed Zarei Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781665402095

Automatic Spoken language identification (LID) refers to the automatic process of identifying languages spoken in the audio files. Pure acoustic approaches have shown great potential in LID. Since acoustic approaches have become more and more popular, phonetic information has been largely overlooked. In this paper, we present a genetic-based fusion approach based on the score probabilities of two phonetic LID systems. There are two SVM classifiers trained on perplexities as their feature vectors which are obtained from phone language models of different phone recognizers. Two phone recognizers are here utilized; one decodes the speech file to a sequence of IPA alphabet, as a universal phone recognizer, and the other is a Farsi phone recognizer which is trained on FARSDAT databases. With the help of the genetic-based fusion approach, we will extract 54 weights. We have 27 languages in our database and 2 individual phonetic LID systems; therefore, we will achieve 54 weights for our fusion. The first 27 weights correspond to our system using a universal phone recognizer and the second 27 weights are related to our system with the Farsi phone recognizer. In the end, we use these weights to combine the results of each of our individual phonetic LID systems. The experimental results conducted on 27 languages within the NIST-LRE09 corpus demonstrated that the proposed fusion approach could greatly increase the classification accuracy of target languages. It should also be noted that we separate the files of each speaker and place them only in one set (train set, development set, or test set) to prevent speaker-related biases.

关键词： Support vector machines Knowledge engineering Databases Speech recognition Phonetics Acoustics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：