检索结果-内蒙古大学图书馆

4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Papadimitriou, Orestis Kanavos, Athanasios Vonitsanos, Gerasimos Maragoudakis, Manolis Mylonas, Phivos Department of Information and Communication Systems Engineering University of the Aegean Samos Greece Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Natural Language Processing (NLP) is increasingly pivotal in the natural sciences, with sentiment analysis emerging as a crucial application in the era of big data. Efficiently and accurately extracting meaningful insights from extensive textual data remains a significant challenge. This study explores sentiment analysis within the context of movie reviews, a domain where understanding nuanced viewer perceptions can influence industry outcomes. Traditional methods often struggle with the complexity of language, where identical phrases may convey different sentiments based on their context. To address these challenges, this research implements a hybrid approach, combining a Multinomial Naive Bayes Classifier and a Long Short-Term Memory (LSTM) model developed in TensorFlow. Our integrated methodology not only advances the robustness of sentiment analysis tools but also achieves a notable accuracy of 96.25%, underscoring its potential to enhance complex NLP tasks in real-world applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Vision Transformer for Audio-Based Depression Detection on Multi-Lingual Audio Data 24

Vision Transformer for Audio-Based Depression Detection on M...

引用

7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Pratiwi, Monica Sanjaya, Samuel Ady Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798400709586

Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddressed or insufficiently addressed depression can result in deteriorating mental well-being, which includes a higher risk of experiencing suicidal ideation and engaging in self-destructive actions. Voice analysis can be employed to distinguish between individuals with depression and those without depression. However, the research that has been conducted on voice recognition to distinguish between depressed and non-depressed individuals keeps focusing on the use of a single dataset source as input for the classification model that is being developed. This work utilizes the Vision Transformer model and various pre-trained transformers, such as DeiT (Data-Efficient Image Transformer) and Swin Transformer, to detect depression based on voice. The objective was to create a more generic model not restricted to a specific language. Integrating Mel-spectrogram characteristics with a vision transformer-based model can enhance the efficacy of voice recognition models when dealing with multi-language data. The result shows 21% higher accuracy than the previous study that also implemented a cross-dataset test. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spectrographs

来源：评论

学校读者我要写书评

暂无评论

Security Strategy of Digital Medical Contents Based on Blockchain in Generative AI Model

引用

computers, Materials & Continua 2025年第1期82卷 259-278页

作者： Hoon Ko Marek R.Ogiela Division of Computer Science and Engineering Sunmoon UniversityAsan31460Republic of Korea AGH University of Krakow Cryptography and Cognitive Informatics LaboratoryKrakow30-059Poland

This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain *** combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical *** participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security *** multi faceted exploration led to an indepth evaluation of the model’s performance and ***,the correlation between accuracy,detection rate,and error rate was *** analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance *** study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical *** blockchain to generative AI-created medical content addresses key personal information protection *** utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical *** approach not only enhances security but also enables transparent and tamperproof ***,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient *** conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient *** proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content *** research and advancements

关键词： Digitalmedical content medical diagnostic visualization security analysis generativeAI blockchain vulnerability pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Enhancing Emotion Classification with a Hybrid BERT and CNN Architecture 19

Enhancing Emotion Classification with a Hybrid BERT and CNN ...

引用

19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Papadimitriou, Orestis Kanavos, Athanasios Vonitsanos, Gerasimos Maragoudakis, Manolis Karkazis, Panagiotis Mylonas, Phivos University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798331504502

In computational linguistics, effectively encoding and systematizing emotional expressions in language is a significant challenge. Existing machine learning (ML) models for text analysis often only recognize primary emotional states such as anger, happiness, and sadness, missing the more nuanced spectrum of human emotions. These models frequently overlook the complex semantic interplays and fail to capture the full diversity of emotional expressions, focusing instead on simplistic categorization. This paper proposes a robust ML framework that enhances emotion classification by discriminating among nuanced emotion categories - Anger, Joy, and Fear. Leveraging a sophisticated combination of Convolutional Neural Networks (CNNs) and the Bidirectional Encoder Representations from Transformers (BERT) architecture, our framework demonstrates exceptional accuracy in emotion detection. Evaluating a diverse dataset of text samples, each meticulously tagged with its expressed emotion, confirms the model's superior performance in recognizing and classifying a broad range of emotional states. © 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

Quantum-Based Prediction Model for Carbon Neutrality 11

Quantum-Based Prediction Model for Carbon Neutrality

引用

11th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2024

作者： Munawar, Ghifari Surendro, Kridanto School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Politeknik Negeri Bandung Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798331520311

Carbon neutrality is a global target pursued by cities worldwide to achieve a balance between carbon emissions and removals, reaching a net-zero carbon state. Mitigation measures are being implemented to reduce emissions and enhance carbon sequestration, aiming to meet the targets set for 2050 or 2060. However, challenges posed by urban sprawl and increasing urbanization raise concerns about the feasibility of achieving carbon neutrality. Various studies have been conducted to project the attainment of this goal by developing prediction models. Machine learning (ML) prediction models use socio-economic, energy, and technological data to forecast carbon neutrality. These models consider factors like GDP per capita, urbanization rate, total energy consumption, and forest stock volume, formulating scenarios based on policy documents and historical data. Some models have incorporated optimization methods like the sparrow search algorithm, genetic neural network, and aquila optimizer to improve prediction accuracy. However, classical optimization methods have limitations, such as susceptibility to getting trapped in local optima, which can affect model performance. Quantum-based optimization methods, particularly quantum annealing (QA), are emerging as potential solutions to address these challenges by leveraging the principles of quantum mechanics to optimize complex problem spaces. QA enhances ML processes like feature selection, hyperparameter optimization, and regression model optimization. This study provides a review of pipeline processes from state-of-the-art methods, as well as their potential quantum-based enhancements, to achieve more precise predictive models. © 2024 IEEE.

关键词： Carbon sequestration

来源：评论

学校读者我要写书评

暂无评论

The contribution of innovative semi-transparent photovoltaics for energy autonomy in the field of greenhouse systems

引用

VDI Berichte 2022年第2406期2022卷 61-68页

作者： Kavga, A. Petrakis, T. Thomopoulos, V. Computer Engineering and Informatics Department University of Patras Patras Greece

Greenhouse systems, which can increase production even in off-season crops without sacrificing quantity or quality, can be a significant benefit for managing the gradual increase in world population. On the other hand, greenhouses' significant energy consumption, whether for heating, cooling, or artificial lighting, cannot be overlooked, accounting for up to 50% of the cost of the product produced. Due to the severity of climate change brought on by the usage of fossil fuels for energy production, and since the agricultural sector is part of a wider political, social, and economic context, it is essential that the energy consumed is produced from environmentally friendly sources, such as solar energy, also known as Renewable Energy Sources – RES. At the same time, the demand for land is constantly increasing, creating both spatial and economic issues. Thus, the installation of semi-transparent photovoltaics on the roof of each construction unit of a greenhouse, replacing the existing cover, leading to double use of the land, is presented as a particularly good solution, for the simultaneous production of both energy and food. In this paper, a Greenhouse Integrated Photovoltaic System (GIPV) is presented, using all the above elements, with the aim of its complete energy autonomy. © 2022, VDI Verlag GMBH. All rights reserved.

关键词： Greenhouses

来源：评论

学校读者我要写书评

暂无评论

Bias analysis of AI models for undergraduate student admissions

引用

Neural Computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Beyond Integration: Advancing Electricity Metering with Secure and Transparent Blockchain-IoT Solutions 47

Beyond Integration: Advancing Electricity Metering with Secu...

引用

47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Koštál, Kristian Bahar, Muhammad Nasim Numyalai, Sultan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350365597

As the demand for reliable and accurate electricity metering escalates in the digital era, traditional systems increasingly need to improve due to their centralized nature, prone to inaccuracies, privacy breaches, and cybersecurity threats. This paper proposes a novel solution that harnesses the transformative power of Blockchain and Internet of Things (IoT) technologies to address these critical challenges. By integrating the immutable ledger capabilities of blockchain with the real-time data acquisition prowess of IoT, we introduce a decentralized system that ensures the secure and transparent measurement and billing of electricity for domestic consumers. Adopting Hyperledger Fabric within our framework offers a robust, scalable architecture that supports secure transactions and enhances trust through digital identities for all participants. This system mitigates the vulnerabilities associated with centralized authorities and efficiently monitors and manages energy consumption with unprecedented precision. Through this approach, we aim to revolutionize the electricity metering infrastructure, providing a blueprint for future advancements in the energy sector. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Satu Sehat Interoperability Agent

Satu Sehat Interoperability Agent

引用

2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Martono, Hero Yudo Yunanto, Andhik Ampuh Santoso, Dian Septiani Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798331511470

Data integration from all health care facilities in Indonesia to the Satu Sehat platform is an obligation that must be carried out, but it turns out that until early October 2024, around 57% of health care facilities have not succeeded in carrying out this integration. With this condition, it can be concluded that there are difficulties for health care facilities to carry out integration and this is estimated to be due to differences in data formats between health care facilities and Satu Sehat. For this reason, an interoperability agent is needed to bridge health care facilities with Satu Sehat. We built the first version of the interoperability agent that bridges this integration. This agent works by entering the client database, searching for the appropriate data in each health care facility, creating a data format that matches the Satu Sehat format, and then sending the format to Satu Sehat. This agent application will evolve over time. For the first version, this interoperability agent has been able to produce Satu Sehat data formats for eight types of resource types including encounter, procedure, condition, composition, medication, medication_request and medication_dispense. Trials have been carried out on each resource type and have run smoothly. In the future, this interoperability agent will be developed so that the number of resource types is in accordance with that determined by Satu Sehat. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Secure & Traceable Companies File Management System Using Blockchain and IPFS 1

Secure & Traceable Companies File Management System Using Bl...

引用

1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Arissabarno, Cahyo Sukaridhoto, Sritrusta Winarno, Idris Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

With the advancement of cloud-based file management platforms, sharing and storing files in the company's area can be easily achieved through a variety of file-sharing platforms. However, these platforms often lack of security and adequate access logs or activity records for the shared files, resulting in a lack of control and traceability for users. This research proposes an traceable and secure system for managing files in companies using blockchain and IPFS. The content ID of uploaded files to IPFS will be stored on the blockchain along with user access rights and access logs. Integration with the existing company file management system is also facilitated by the use of the change data capture method. Experimental findings demonstrate that the system offers the desired traceability of access and activity while also meeting data security requirements such as confidentiality, integrity, and availability. Furthermore, performance tests indicate satisfactory outcomes with 0.8 seconds per MB for file storage on IPFS, and 4.1 seconds per transaction for data storage on Blockchain. The experimental results revealed that the proposed system, integrating blockchain and IPFS, successfully addressed the need for traceability of access and activity for the company's data, providing a significant improvement in data security and management over existing solutions. © 2024 IEEE.

关键词： Cloud platforms

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：