检索结果-内蒙古大学图书馆

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Boopalan, Sunil SRM Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798331542344

This research paper further builds on previous research by proposing a combination of AI and blockchain in the context of integration of quantum computing to improve the cybersecurity of cloud and financial operations, especially for India's UPI and digital rupee platforms. This is especially the case when digital transactions are on the rise and their security against rampant cyber threats and overall soundness of operations must be guaranteed. The following is a novel framework that embellishes AI, blockchain, and quantum computing to meet these challenges outlined in this paper. Machine learning and pattern recognition will be implemented to scope out and prevent security threats, while blockchain is to ensure a secure and open system of transactions;quantum computing will help to provide extraordinary features for data security and data encryption. The research also seeks to examine the current implementation approaches, advantages, and disbursement issues of this integration and subsequently makes recommendations that can help policymakers and practitioners make improvements for the improvement of trust and security in the implementation of financial solutions on the cloud. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Sentiment Analysis of Product Reviews

Multi-Modal Sentiment Analysis of Product Reviews

引用

2024 International Conference on computer, Electronics, Electrical Engineering and their Applications, IC2E3 2024

作者： Gorai, Joy Shaw, Dilip Kumar National Institute of Technology Department of Computer Science and Engineering Jamshedpur India

ISBN: (纸本)9798350388534

With the exponential growth of e-commerce, understanding consumer sentiments from online product reviews has become crucial for businesses. This study presents a comprehensive approach to online product review sentiment analysis, encompassing text and image data. To facilitate our research, we compiled a novel dataset from the Amazon e-commerce platform, comprising paired textual reviews, corresponding images and sentiment. The work employs a multimodal deep-learning approach to capture the nuanced sentiment expressed through both text and images. Specifically, we integrate the Bidirectional Encoder Representations from Transformers (BERT) model, enhanced with a low-rank adaptation to refine its self-attention mechanism, for textual sentiment analysis and the VGG16 neural network for image feature extraction. Our experiments demonstrate that this multimodal approach surpasses unimodal techniques, achieving convincing results in sentiment classification. © 2024 IEEE.

关键词： Feature fusion Low rank adaption Multimodal Product review

来源：评论

学校读者我要写书评

暂无评论

Speculative Analysis of CNN's and ResNet50 for the Identification of Emotion From Facial Expressions 9

Speculative Analysis of CNN's and ResNet50 for the Identific...

引用

9th International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Kumar, Bathinapatla Hemanth Gokila, S. Hindustan Institute of Technology And Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350365092

Facial recognition software has become essential in several domains, such as security and surveillance and smart device user identification. This paper looks at the effectiveness of deep learning methods for facial recognition to classify images based on the emotion, with a particular focus on Convolutional Neural Networks (CNN). A condensed CNN and ResNet50 are the two CNN architectures whose impacts on loss values, processing efficiency, and accuracy are specifically evaluated. A variety of face datasets containing images depicting seven distinct emotional states - Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral - are used to train and assess the models. Assessment metrics, such as calculation time, accuracy, and loss values, are employed to evaluate the effectiveness of every technique. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Passengers Monitoring Models at Jakarta Bus Stops Using the YOLOv3

Passengers Monitoring Models at Jakarta Bus Stops Using the ...

引用

2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Jessen Hadisurya, Nicolaas Wulandhari, Lili Ayu Alam, Islam Nur School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368451

Public transportation plays a crucial role in supporting urban mobility in big cities such as Jakarta. This condition can lead to overcrowding issue at bus stops, especially during working hours, such as in the morning when people go to work or in the evening when people are going home. The objective of this research is to analyze the density pattern at S. Parman Transjakarta bus stop, during the evening, to determine important strategies for addressing the overcrowding issue. The AI model that was proposed and used in this study was based on a YOLOv3 algorithm that was trained on the customized COCO dataset. Our dataset will contain 2000 images that contain at least 10 people, then we will split it into a training set and testing set. This model was utilized for detecting the number of passengers from photographs taken over a span of five consecutive days, from Monday through Friday. The result shows that the number of passengers is not constant and varies by the day of the week. This finding was expected to be a source of knowledge that can help in improving the efficiency of public transportation services as well as the passengers' experience. © 2024 IEEE.

关键词： Bus terminals

来源：评论

学校读者我要写书评

暂无评论

Early Melanoma Skin Cancer Detection Using Artificial Intelligence: A Comparative Review 9

Early Melanoma Skin Cancer Detection Using Artificial Intell...

引用

9th International Conference on Information Management and technology, ICIMTech 2024

作者： Aqmarina, Nazhira Dewi Christiano, Lin Dan Adiwinata, Regina Celine Pangestu, Gusti School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Cancer is a deadly disease if not treated seriously, including the most visible part of the body, the skin. Melanoma is a deadly type of skin cancer. If this type of cancer is not treated quickly, it is likely to spread to other parts of the body and cause death. Artificial Intelligence can help humans overcome the problem of Melanoma by detecting it so that it can be treated as quickly as possible. This paper focuses on finding the model with the highest accuracy and knowing the factors that influence the accuracy value. This research uses the Systematic Literature Review (SLR) method, where the authors will search for papers related to the topic and research questions, review, analyze, and provide detailed conclusions on these papers. Of the hundreds of papers covering the topic, eight that met the criteria were taken for further analysis to find the best model accuracy. Among the models that demonstrate a high degree of accuracy are VGG19, ResNet-18, and ResNet-50, with 97.5%, 94.47%, and 93.96%, respectively. The selection of a suitable dataset strengthens the accuracy of the model. The results show that the HAM10000, ISIC 2019, and ISIC 2020 datasets are three datasets that produce high accuracy when used for matching models. So, factors influence the production of precise accuracy, such as the selection of models and datasets. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

来源：评论

学校读者我要写书评

暂无评论

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

引用

computer Modeling in Engineering & sciences 2024年第2期138卷 1427-1467页

作者： Mohammed Abdalsalam Chunlin Li Abdelghani Dahou Natalia Kryvinska School of Computer Science and Technology Wuhan University of TechnologyWuhan430070China LDDI Laboratory Faculty of Science and TechnologyUniversity of Ahmed DRAIAAdrar01000Algeria Information Systems Department Faculty of ManagementComenius UniversityBratislava82005Slovakia

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

关键词： Artificial intelligence machine learning natural language processing data analytic DistilBERT feature extraction terrorism classification GTD dataset

来源：评论

学校读者我要写书评

暂无评论

Analysis Fake News Detection Models Using Swarm Intelligence 8

Analysis Fake News Detection Models Using Swarm Intelligence

引用

8th International Conference on Information technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Deliansyah, Heru Gumilang, Zaky Satya Alfayed, Muhammad Iswanto, Irene Anindaputri Hidayat, Muhammad Fadlan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368970

The proliferation of fake news on social media platforms has become a significant societal challenge, undermining public discourse and democratic processes. This study proposes a novel approach to fake news detection by leveraging swarm intelligence techniques, specifically a Modified Ant Colony Optimization (ACO) algorithm, in combination with established machine learning models. Our method employs a four-stage process: data preparation, feature extraction using TF-IDF vectorization, feature selection via Modified ACO, and model evaluation. We compare the performance of Random Forest, XGBoost, and Support Vector Classifier (SVC) models before and after dimensionality reduction. The WELFake dataset, comprising 72,134 news articles, is used to train and evaluate the models. Results demonstrate that the proposed approach achieves high accuracy in fake news detection while significantly reducing computational time, particularly after dimensionality reduction. This research contributes to the development of more efficient and accurate fake news detection systems, addressing the critical need for robust tools to combat misinformation in the digital age. © 2024 IEEE.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

X-Ray Based Lung Disease Classification Using Fine-Tuned VGG16 Model 2

X-Ray Based Lung Disease Classification Using Fine-Tuned VGG...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gehani, Ronit Victor, Akila Vellore Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350327533

The COVID-19 pandemic was a global outbreak of a viral that caused widespread to people's well-being. COVID-19 primarily affects the respiratory system, leading to various lung diseases and complications. During this unprecedented time, the need for accurate and efficient classification systems for lung diseases, including COVID-19, has become more crucial than ever. During the pandemic, accurate and timely identification of lung diseases became crucial in order to effectively manage the healthcare system and provide appropriate treatment to patients. Therefore, early detection using state-of-The-Art technology such as deep learning and computer vision algorithms can greatly assist in the classification of lung diseases. Concurrently there has been an unprecedented increase in the incidence of other respiratory ailments, such as Pneumonia, which further emphasizes the need for accurate and efficient classification systems. In light of this urgent requirement, this study has proposed a novel model for image classification by demonstrating the ability to differentiate COVID-19, viral pneumonia, and normal categories. The classification model presented in this study is constructed by fine-Tuning a VGG16 architecture that has undergone extensive training, validation, and evaluation processes. The proposed dataset contains a large set of images, which includes 3616 COVID-19 images, 1345 Pneumonia and 10k normal training images. According to the experimental findings, the proposed model has achieved an accuracy of 95.79%. To verify the performance, the training and validation loss curves are outlined. © 2024 IEEE.

关键词： Bayesian Optimizer COVID-19 Deep learning Feature Extraction Feature Selection Machine learning Prediction X-ray images

来源：评论

学校读者我要写书评

暂无评论

Molecule World: Enhancing chemistry education through web-based augmented reality using Assemblr 9th

Molecule World: Enhancing chemistry education through web-ba...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Handoyo, Kevin Joseph Wisnuwardana, Christian Jodi Austen, Aaron Permana, Frihandhika Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Our research explores the role of web-based AR, namely Assemblr, in creating an interactive, engaging, and immersive learning environment. The focus of this research is how the use of AR can motivate, engage, and increase students' interest in learning in the field of education, especially in the field of chemistry on molecules and compounds. The general impression of the Molecule World app, built within the ADDIE instructional design framework, significantly improved the user experience. The attractiveness of the app achieved a benchmark score of 1.88, dependability a benchmark score of 1.73, and novelty a benchmark score of 1.65. Perspicuity can be improved with a benchmark score of 2.00, efficiency of 1.80, and stimulation of 1.63. The results of our research show that augmented reality technology can be an alternative for the world of education, especially in the field of chemistry, to increase the attractiveness and interest of student learning. We hope that this research can open new insights related to the application of immersive technology, namely augmented reality, which is implemented into the world of education to improve the quality of chemistry learning. © 2024 The Authors.

关键词： Students

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：