检索结果-内蒙古大学图书馆

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

来源：评论

学校读者我要写书评

暂无评论

IoT Security Challenges in Modern Smart Cities

IoT Security Challenges in Modern Smart Cities

引用

Cyber Resilience (ICCR), International Conference on

作者： Mohammad Alauthman Amjad Aldweesh Ahmad Al-Qerem Department of Information Security University of Petra Amman Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper provides an overview of IoT security challenges in smart cities. We examine cyber threats across the device, network, platform and data layers, analyzing real-world attacks that have disrupted critical municipal services (case studies). Statistics on expected global IoT growth highlight proliferation concerns (statistical data analysis). Key technical vulnerabilities like insecure interfaces, unpatched firmware and inadequate encryption are explored through examples (comparative analysis). A multilayered security approach is proposed encompassing network protections, access controls, data privacy, continuous monitoring and innovative technologies (conceptual framework). Additionally, the paper presents a comparative analysis of wireless protocols used in city IoT deployments (technical comparison). Overall, both practical incidents and technical assessments demonstrate the necessity of cybersecurity strategies tailored to IoT scale and heterogeneity in smart city contexts (supported claims). Recommendations enable resilient innovation.

关键词： Access control Technological innovation Protocols Technical management Smart cities Wireless networks Threat assessment

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

引用

computers, Materials & Continua 2022年第4期71卷 109-124页

作者： Sathish Babu Pandu A.Sagai Francis Britto Pudi Sekhar P.Vijayarajan Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Mechanical Engineering Rohini College of Engineering&TechnologyPalkulam629401India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyAndra Pradesh530046India Department of Electrical and Electronics Engineering University College of EngineeringBIT CampusTiruchirappalli620024India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the *** Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation *** models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of *** this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and ***,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting *** order to perform long-term collection of weather data,Hadoop MapReduce tool is *** proposed solar irradiance prediction model operates on different ***,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data ***,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive *** is divided into input layer,hidden layer,loadbearing layer,and output *** overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is *** order to validate the performance of the proposed model,a series of experiments was *** experimental values infer that the proposed model outperformed other methods used for comparison.

关键词： Solar irradiation prediction weather forecast artificial intelligence Elman neural network mayfly optimization

来源：评论

学校读者我要写书评

暂无评论

Attention-based Memory Fusion Network for Clinical Outcome Prediction using Electronic Medical Records

Attention-based Memory Fusion Network for Clinical Outcome P...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Al-Dailami, Abdulrahman Kuang, Hulin Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha410083 China Sana'a University Faculty of Computers and Information Technology Sana'a Yemen

ISBN: (纸本)9781665468190

Recent methods of patient clinical outcome prediction focus on embedding the temporal time-series data by sequential data encoders without considering the dependency between the different variables and the static demographics data. To solve this problem and achieve better patient outcome prediction, we propose an attention-based memory fusion (AMF) network with Gated Recurrent Unit (GRU) (called GRU-AMFN) to model the dependency between the different time-series and static demographic data and extract effective personalized representation about the patient's clinical health status. We evaluate our proposed GRU-AMFN method on eICU, a publicly available dataset, to validate its effectiveness for the in-hospital mortality prediction task. Experimental results demonstrate that our proposed method outperforms several state-of-the-art models for the in-hospital mortality prediction task. Ablation studies show the effectiveness of the proposed attention-based memory fusion module and the adaptive fusion module. Besides, our proposed method finds several static demographic and time-series features that are important for mortality prediction. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

引用

7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Feature enhancement model with up sampling based cyber threat attack detection and classification on imbalanced dataset in Industrial Internet of Things

引用

Alexandria Engineering Journal 2025年 128卷 247-258页

作者： Hayam Alamro Fahd N. Al-Wesabi Sultan Alahmari Jawhara Aljabri Shouki A. Ebad Asma Alshuhail Fouad Shoie Alallah Abdulrhman Alshareef Mahir Mohammed Sharif Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia King Abdul Aziz City for Science and Technology (KACST) Cybersecurity Institute Riyadh Saudi Arabia Department of Computer Science University College in Umluj University of Tabuk Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia

With the fast growth and utilization of artificial intelligence (AI) models, the Industrial Internet of Things (IIoT) has remarkably progressed in locating industrial communication and enhancing industrial methods quickly. In Industry 5.0, the hyper-automation method is a technical trend that navigates manufacturing objects to intellectual devices of IIoT, smart, agile software, cloud computing, smart robotics, and embedded modules by reliability and higher intricacy. Machine learning (ML) and deep learning (DL) methods were established for identifying anomalies by understanding the usual behaviour methods of the cyber threat attack and identifying and detecting deviations. This study proposes a Feature Enhancement Model with a White Shark Optimizer-based Cyber Threat Attack Detection and Classification (FEWSO-CTADC) technique on an Imbalanced Dataset in an IIoT environment. The primary focus of the FEWSO-CTADC technique is to enhance the automatic classification and detection of cyber threats in the IIoT environment. Initially, the FEWSO-CTADC technique implements a data preprocessing model to scale the raw information into a uniform format. Next, the SMOTE technique is used to manage the imbalanced class distribution in the attack recognition database. Moreover, the WSO-based feature subset selection is accomplished to select the superior set of features. Finally, the FEWSO-CTADC method utilizes the stacked auto-encoder (SAE) method for attack classification and recognition. Extensive experiments were conducted to demonstrate the improved performance of the FEWSO-CTADC approach, and the results were compared across various methods. The performance validation of the FEWSO-CTADC approach exhibited a superior value of 99.20 % over recent techniques under diverse metrics.

关键词： Cyber threat Stacked auto-encoder White shark optimizer Feature enhancement model Industrial Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Shaping generative AI governance in higher education: Insights from student perception

引用

International Journal of Educational Research Open 2025年 8卷

作者： Barus, Okky Putra Hidayanto, Achmad Nizar Handri, Eko Yon Sensuse, Dana Indra Yaiprasert, Chairote Faculty of Information Technology Universitas Pelita Harapan Medan Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia School of Science Walailak University Nakhon Si Thammarat Thasala Thailand

This study explores student perspectives on generative AI governance in higher education to ensure responsible and ethical AI integration. Employing a mixed-methods approach with an explanatory sequential design, the study gathered data from 111 undergraduate students at Universitas Pelita Harapan through an online survey and semi-structured interviews with 53 students. The study covered key aspects of AI governance, such as ethics, curriculum integration, misuse detection, and academic sanctions, which are crucial for ensuring responsible AI implementation. The findings reveal that students are highly aware of the potential benefits of Generative AI and support its integration into the curriculum. However, they also emphasize the need for clear guidelines, ethics training, and plagiarism detection mechanisms to prevent misuse and uphold academic integrity. These findings provide valuable insights for higher education institutions to develop a comprehensive AI governance framework that balances the potential of Generative AI with the ethical considerations of its use. © 2025 The Authors

关键词： Ethics Generative AI Governance Higher education Student perspective

来源：评论

学校读者我要写书评

暂无评论

Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

引用

Computing, Robotics and System sciences (ICRSS), International Conference on

作者： Shahid Karim Muhammad Ibrar Anas Bilal Akeel Qadir Irfana Bibi School of Information Engineering Xi'an Eurasia University Xi'an China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Lahore Punjab Pakistan

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou _ nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection.

关键词： YOLO Training Accuracy Sensitivity Service robots Neural networks Fires Clustering algorithms Smart homes Safety

来源：评论

学校读者我要写书评

暂无评论

Masked Face Recognition by Applying SSD and ResNet Model for Attendance System

Masked Face Recognition by Applying SSD and ResNet Model for...

引用

2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Efanntyo Mitra, Aditya R. Universitas Pelita Harapan Electrical Engineering Faculty of Science and Technology Tangerang Indonesia Universitas Pelita Harapan Information Systems Faculty of Computer Science Tangerang Indonesia

ISBN: (纸本)9781665499699

The rapid development of technology in artificial intelligence (AI) includes face recognition. Some of these developments in face recognition systems can be found easily in everyday life, such as entry access, payment, or attendance recording systems. Notably, the demand for image-based face recognition methods to record attendance increases due to its effectiveness and efficiency. Compared to conventional methods like fingerprint or RFID, facial recognition offers better results. On the other hand, with the arising needs in dealing with the novel coronavirus (COVID-19) pandemic, official health protocols require mask-wearing and maintaining a minimum distance of 1 meter between individuals to prevent the spread of the virus. Along with such a situation, a face recognition system to record attendance can reduce the occurrence of direct contact and allow each individual to maintain a safe distance, including from the attendance device. This paper presents the performance of a masked-faced recognition system that implemented SSD (Single Shot Detection) and ResNet feature extraction. The face recognition system application developed using Python and related libraries show a stable level of masked face recognition accuracy. This evaluation was made at predetermined distances between the face and the camera and measured at room lighting of 200 lux with an average accuracy of 67%. The application also has a feature to send notification emails to every employee who is unable to attend work on their scheduled workdays. © 2021 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Off-Shelf Deep Learning Architectures as Feature Extractor in CT COVID-19 Classification

Off-Shelf Deep Learning Architectures as Feature Extractor i...

引用

Emerging Trends in Computing and Engineering Applications (ETCEA), International Conference on

作者： Haythem El-Messiry College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Departement of Computer Science Faculty of Computer and Information Science Ainsham university

ISBN: (纸本)9781665477109

Recently the chest imaging plays an important role in COVID-19 diagnosis compared to laboratory diagnosis such as RT-PCR. This paper investigates a robust algorithm to detect infected COVID-19 patients using computed tomography scans. The proposed algorithm utilizes a deep learning approach through applying an off shelf pretrained neural network to extract a feature map matrix from the first layer of convolutional neural network, which preserve the basic features related to geometry structures. Later the trained features are classified using a machine learning classifier as support vector machine algorithm to classify the tested images into two classes infected against not infected. The investigated algorithm was trained and tested using two open-source datasets. The results were experimented using five general pretrained off shelf CNN architectures, the performance of the pretrained algorithm was measured and evaluated for the extracted image features with popular five pretrained CNN and classification accuracy 90%.

关键词： COVID-19 Support vector machines Deep learning Machine learning algorithms Sensitivity Computational modeling Computed tomography

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：