检索结果-内蒙古大学图书馆

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Dhobale, Nikita Dinkar Elle, Krisha Ramesh Deshmukh, Pratiksha Vilas Nirne, Sakshi Sidram Jarali, Prof. Ashwini International Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350379945

The system entails the development of a sentiment analysis system consisting of two integral modules aimed at analysing audio recordings of calls. The first module predicts sentiment-positive, negative, or neutral-by converting speech to text using speech-to-text APIs and subsequently applying machine learning algorithms including Random Forest, Naive Bayes, SVM, and Logistic Regression. The second module employs audio processing techniques utilising Librosa for feature extraction and Convolutional Neural Networks (CNNs) for emotion recognition. The system facilitates comparisons between the results obtained from both modules for call recordings. Users, starting with system administrators, can choose between two options: browsing for uploading pre-recorded audio files or live recording. In both scenarios, the system executes sentiment analysis and emotion detection, presenting results derived from speech-to-text and audio processing analyses. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Lightweight irregular driving behavior detection algorithm based on star operation 5

Lightweight irregular driving behavior detection algorithm b...

引用

5th International Conference on computer Vision and Data Mining, ICCVDM 2024

作者： Liang, Jiaxin Department of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9781510682863

To address the problems of high memory consumption, heavy computational burden, and difficulty deploying existing detection algorithms on edge devices, a lightweight approach for detecting abnormal driving behavior based on an updated YOLOv8 model has been presented. Firstly, the backbone network is replaced with the StarNet network, which has lower computational and memory access requirements. Second, utilizing an enhanced weighted bidirectional feature pyramid network with C2f blocks enhances the model's capability to capture multi-scale features swiftly. Finally, an asymmetric multi-level compression detection head is introduced to reduce the model size further. The experimental findings demonstrate that the suggested enhanced model achieves a 73.2% reduction in parameters, a 60.9% decrease in computational burden, and a 72.8% reduction in model size while retaining the same accuracy as the original YOLOv8 model. This significantly reduces hardware costs, demonstrating excellent lightweight effects. © 2024 SPIE.

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

Fuzzy validation of Taguchi-based convolutional fuzzy neural classifier for lung cancer imaging

引用

Multimedia Tools and Applications 2024年第41期83卷 89415-89437页

作者： Chang, Tsang-Chuan Lin, Cheng-Jian Yang, Tang-Yun Department of Intelligent Production Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411030 Taiwan

While deep learning technology is widely used in the field of image classification and recognition, parameter setting for convolutional neural networks is complex, and a high number of parameters make the technology difficult to apply in practice. Therefore, this study proposes a Taguchi-based convolutional fuzzy neural classifier (T-CFNC) to classify computed tomography (CT) images. Two layers of convolution and pooling are used to extract features of the input images, and a fuzzy neural network is used to replace fully-connected neural networks to reduce the number of model parameters. To reduce cost and time, the Taguchi experimental design method determines the optimal combination of model parameters with the minimal number of experiments. The SPIE-AAPM lung CT challenge dataset was used to validate the proposed T-CFNC model. Experimental results indicate accuracy of 99.95%, a true positive rate of 99.97%, and a true negative rate of 99.94%. While the confusion matrix is commonly applied to evaluate model performance, its accuracy varies with the quality of model training, and it is easily affected by extreme values. Single and average values also incur the possibility of misjudgment. We therefore further propose fuzzy validation for performance evaluation. Results confirm the superiority of the proposed T-CFNC model in terms of lung cancer image classification over the traditional CFNC model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Leveraging a structure-based and learning-based predictor using various feature groups in bioinformatics (case study: protein-peptide region residue-level interaction) 14

Leveraging a structure-based and learning-based predictor us...

引用

14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Shafiee, Shima Fathi, Abdolhossein Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

ISBN: (纸本)9798331511272

Predicting protein-peptide interactions is crucial for understanding cellular activities, researching protein functions, designing new drugs, studying abnormal cell behavior, and addressing human diseases. Conventional experimental techniques for identifying the residues in protein-peptide interaction regions are labor-intensive and costly. Therefore, determining these binding region residues using computational methods would serve as a valuable and complementary tool. Our research introduces RSPPRI, a computational method that combines a Residual Neural Network and a Support Vector Machine to accurately identify the residues in the binding regions of protein-peptide interactions. To achieve this, we extract various feature groups from protein structures, including evolutionary, physicochemical, and structure-based features. ©2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Noise Estimation from Degraded Speech

Noise Estimation from Degraded Speech

引用

International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Thorbole, Shubham Raj, Rahul Upadhyay, Navneet Department of Electronics and Communication Engineering The LNM Institute of Information Technology Jaipur India Department of Computer and Communication Engineering The LNM Institute of Information Technology Jaipur India

ISBN: (纸本)9789819778751

In real-world scenarios, speech signals are often corrupted by various types of noise, which can significantly degrade the intelligibility and quality of the speech. Noise in such environments is highly non-stationary, changing rapidly and unpredictably. A new algorithm has been created to estimate noise in speech signals affected by rapidly changing and unpredictable non-stationary noise. This algorithm aims to enhance speech quality and intelligibility in challenging acoustic settings. It determines signal presence by comparing the power spectrum of noisy speech to a continually updated local minimum, calculated from past noisy speech power spectra with a look-ahead factor. Its standout feature is its rapid adaptability to swiftly respond to new noise patterns, crucial for maintaining speech clarity and quality in dynamic noise environments. Formal experiments validated its effectiveness compared to other noise-estimation methods when integrated into speech enhancement systems, using metrics like mean squared error (MSE) and signal to noise ratio (SNR). This algorithm significantly contributes to noise estimation and voice enhancement, offering effectiveness in handling highly non-stationary noise and being adaptable and versatile for various applications. This algorithm stands as a substantial advancement in the domains of noise estimation and voice enhancement overall. Its utility extends across diverse applications owing to its proficiency in handling highly non-stationary noise conditions, coupled with its adaptability and versatility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Background noise

来源：评论

学校读者我要写书评

暂无评论

Vision Aid For Blind People Using YOLOV8 2

Vision Aid For Blind People Using YOLOV8

引用

2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： More, Priyanka Sangamkar, Satvik Vishwakarma Institute Of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367461

This assistive system for the visually impaired integrates real-time YOLOv8 object detection with distance estimation and text-to-speech conversion, empowering users with enriched environmental awareness. Leveraging robust datasets and architectures, it identifies over 90 everyday objects amidst clutter while concurrently approximating object proximity through trigonometric translations of size metrics. Audio cues enunciate recognized items and distances in real time for accessibility. This combination of precise visual recognition, spatial metrics, and assistive feedback holds promise for enhanced mobility, independence, and safety during navigation for visually impaired individuals. © 2024 IEEE.

关键词： Vision aids

来源：评论

学校读者我要写书评

暂无评论

An Improved Dense Crowd Detection Model Based on YOLOv8 18

An Improved Dense Crowd Detection Model Based on YOLOv8

引用

18th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Lin, Zewei Xu, Huarong Xiamen University of Technology Department of Computer and Information Engineering Xiamen China

ISBN: (纸本)9798331505639

With the increase of crowd monitoring and management needs, dense crowd detection has become an important research direction in the field of computer vision, mainly using target detection methods to detect the position and number of pedestrians in pictures and videos. Aiming at the problem of missed detection and misdetection due to the occlusion between pedestrians or the target pedestrians being too small in dense crowd scenes, this paper proposes an improved detection algorithm CscSCA-YOLOv8 based on YOLOv8. Firstly, the Conv-SPD-Conv module is used instead of each convolutional step and each pooling layer in the trunk and the neck, to prevent the loss of fine-grained information and the less efficient feature representation Learning. Then the SCAMix hybrid attention mechanism is introduced into the YOLOv8 network architecture to utilize the global perception capability of self-attention and capture local features via CoordAttention to improve detection in dense scenes. Experimental results show that this paper's method achieves 74.1% and 46.6% for mAP50 and mAP50-95 on the WiderPerson dataset, respectively, and improves 3.6% and 2.8% compared with the original YOLOv8n. © 2024 IEEE.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

引用

Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

引用

computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

来源：评论

学校读者我要写书评

暂无评论

Advancements in Depression Severity Prediction: A Multi-Modal Machine Learning Approach Integrating MCQs and Audio Data 2

Advancements in Depression Severity Prediction: A Multi-Moda...

引用

2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Pimpalkar, Shilpa P. Chavan, Anish Avinash Gawali, Sadhana Vijay Dalvi, Shreya Sanjay Department of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350383867

This research presents a machine-learning framework for predicting depression severity by leveraging Multiple-Choice Questions (MCQs) and transcribed audio data. Two distinct data sources are employed, with regression techniques (linear, decision tree, random forest, support vector) applied to both MCQs and audio data. The severity predictions from individual regression models are amalgamated using a weighted average. Additionally, a combined machine learning model integrates classification algorithms (logistic regression, decision tree classification, random forest classification, support vector classification) for both MCQ and audio data. Trained separately, these models contribute to an integrated prediction of depression severity levels-minimal, mild, moderate, and severe. The MCQs utilized originate from the Beck Depression Inventory-2 (BDI-2) questionnaire. This comprehensive approach, encompassing subjective self-report and objective audio indicators, aims to enhance the accuracy of depression severity predictions, offering a valuable tool for early detection and intervention in mental health. The first key component of our approach involves utilizing the structured information derived from the Beck Depression Inventory-2 (BDI-2) questionnaire, which is represented by MCQs. This well-established instrument is widely used for assessing depression, providing a standardized set of questions that capture self-reported symptoms and emotional states. Leveraging the richness of MCQ responses, we employ regression techniques such as linear regression, decision tree regression, random forest regression, and support vector regression to predict the severity level of depression for each participant. © 2024 IEEE.

关键词： Prediction models

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：