检索结果-内蒙古大学图书馆

2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Thakur, Sanjeev Bele, Rohit Yarlagadda, Tarun Chaubey, Ved Prakash Sharma, Shamneesh Gochhait, Saikat School of Computer Science and Engineering Lovely Professional University Punjab Jalandhar India Delivery and Student Success upGrad Education Private Limited Karnataka Bangalore India SIDTM Symbiosis International Deemed University Maharashtra Pune India NRI Samara State Medical University Russia

ISBN: (纸本)9798331533137

This paper analyzes the Speech Emotion Recognition (SER) procedure, a tool that helps IT systems increase their intelligence by recognizing persons’ emotions from the signals put into words, by employing deep learning methods. The audio data was preprocessed by extracting the Mel-frequency cepstral coefficients (MFCCs) and later augmented by adding white noise, time shifting, pitch shifting and time stretching. The models used in this paper are Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs). CNN architecture has convolutional operation layers, batch normalization layers followed by max-pooling layers, and dropout layers to learn speech features and to avoid overfitting. With these results, we stress the importance of the synergy of CNNs with solid preprocessing and augmentation methods in the context of SER and envision their tremendous potential in HCI, education, and health. It is worth stressing that including models such as SVM and LSTM certifies the novelty and the genetic soundness of the approach. ©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

Binary Particle Swarm Optimization Algorithm Based on Z-shaped Probability Transfer Function to Solve 0-1 Knapsack Problem

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2021年第2期48卷 1-9页

作者： Sun, Wei-Zhong Zhang, Min Wang, Jie-Sheng Guo, Sha-Sha Wang, Min Hao, Wen-Kuo A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of International Finance and Banking University of Science and Technology Liaoning Anshan114051 China

Binary particle swarm optimization (BPSO) algorithm can map the original continuous searching space to the binary searching space by introducing a new velocity transfer function. For the 0-1 knapsack problem, a binary particle swarm optimization algorithm based on the Z-shaped probability transfer function was proposed. In order to solve the shortcomings of BPSO algorithm based on S-shaped and V-shaped probability functions that it is easy to fall into local optima and slow convergence speed, a new probability function (Z-shaped transfer function) was proposed. Then a penalty function strategy is adopted to deal with the violation of the constraint solutions. In order to verify the effectiveness of the proposed algorithm, the BPSO algorithm based on the Z-shaped transfer function with different parameters was used to solve the typical 0-1 knapsack problems, which is compared with the BPSO algorithm based on the S-shape and the V-shape transfer functions. Simulation experiment results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm. © 2021. All Rights Reserved.

关键词： Transfer functions

来源：评论

学校读者我要写书评

暂无评论

Towards Safer Environments: A Yolo and Mediapipe-Based Human Fall Detection System with Alert Automation

SSRN

引用

SSRN 2025年

作者： Chakurkar, Priti S. Kothari, Virag Pradip Student Dr.Vishwanath Karad MIT WORLD PEACE UNIVERSITY School of Computer Science and Engineering Kothrud Maharashtra Pune India Dr.Vishwanath Karad MIT WORLD PEACE UNIVERSITY School of Computer Science and Engineering Kothrud Maharashtra Pune India

Detecting human falls is essential to ensuring public safety, particularly in public areas like transit terminals. This study provides a precise and effective real-time fall detection system by utilising pose estimation and deep learning-based object detection approaches. The system correctly detects falls in dynamic circumstances by combining MediaPipe's pose estimate for in-depth body posture analysis with the YOLOv8 model for human recognition. The paper provides a novel method that improves the system's scalability and robustness in real-world scenarios by utilising position landmarks and activity identification algorithms. To enable accurate fall detection and reduce false positives, the system also makes use of anomaly detection techniques. The system uses Twilio to send out real-time warnings as soon as a fall is detected, sending out video footage of the incident and alerting the appropriate authorities. The system is an excellent option for enhancing safety in sizable public areas because to its effectiveness, scalability, and privacy-preserving features. Metrics like accuracy, precision and F1-score are used in the study to assess the system's performance and show its usefulness. © 2025, The Authors. All rights reserved.

关键词： Fall detection

来源：评论

学校读者我要写书评

暂无评论

Data storage security issues in cloud computing

Data storage security issues in cloud computing

引用

2021 International Conference on Innovative Practices in Technology and Management, ICIPTM 2021

作者： Singh, Devang Pratap Kaushik, Prakarsh Jain, Manjari Tiwari, Vidushi Rajpoot, Subhadra Amity School of Engineering and Technology Student of Computer Science and Engineering Greater Noida India Amity School of Engineering and Technology Department of Applied Science Greater Noida India

ISBN: (纸本)9780738132891

Cloud computing gives on request administration on their information or data to its customers. Information storage(storing) is among one of the essential administration given by the cloud computing. Cloud specialist organization has the information of information proprietor on their server and client can get to their information from these servers. As information, proprietors and servers are various personalities, the worldview of information storage raises numerous security challenges. A free component is required to ensure that information is effectively facilitated to the cloud storage server. Right now, we will examine the various methods that are utilized for secure information storage on cloud. © 2021 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Accurate Classroom Person Detection Based on Multi-Frame Feature Fusion with Attention 8

Accurate Classroom Person Detection Based on Multi-Frame Fea...

引用

8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Cheng, Guoan Ren, Jiandong Li, Yuping Wang, Shengke Ding, Yougang Wang, Hao School of Information & Electrical Engineering Qingdao Harbour Vocational & Technical College Qingdao China Qingdao Harbour Vocational & Technical College Student Work Department Qingdao China College of Computer Science and Engineering Ocean University of China Qingdao China Experimental School Affiliated to Shandong University Mathematics Teaching and Research Group Heze China

ISBN: (纸本)9798350335231

In recent years, object detection technology has developed rapidly and is widely used in the field of intelligent video monitoring, especially in the classroom scenes, where incorporating the intelligent object detection technique into the video monitoring systems for intelligent monitoring purpose has important practical significance. However, surveillance videos may encounter issues such as object occlusion, motion blur, posture changes, and video defocus, which poses great challenges to object detection in the surveillance video scenarios. Therefore, in this paper, we conduct in-depth research on human detection in classroom multi camera surveillance video scenes and propose a multi frame feature fusion network based on the attention mechanism. The algorithm first adds the dilated convolution layers to the ResNet50 backbone network to expand the receptive field and retain more feature information;Secondly, a multi frame feature fusion strategy based on the attention mechanism was proposed to enhance the feature representation ability of the deep detector by fusing the feature information of adjacent frames, thereby improving the accuracy of human detection in the classroom monitoring video. To verify the effectiveness of the proposed method, we have compiled and produced a large-scale classroom monitoring video dataset, named OUC-ClassVideo. The proposed object detection network, which is developed based on the attention mechanism and multi frame feature fusion strategy, has achieved satisfactory performance on both the public dataset ImageNet VID and the OUC-ClassVideo dataset collected by ourselves. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Feature Extraction, Essential Dimension Estimation and Dimension Reduction Method of Colony Images

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2021年第2期48卷 1-13页

作者： Sun, Wei-Zhong Guo, Fu-Jun Wang, Jie-Sheng Chen, Lin Wei, Dong Du, Xin-Feng A lecturer of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China A postgraduate student of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China A professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China An associate professor of School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

A colony is a group of microorganisms produced by the growth and reproduction of a single microorganism species. A method of colony image feature extraction, essential dimension estimation and dimension reduction based on digital image processing technology was proposed. Firstly, based on the HSI image color moments (first, second and third order moments), nine color characteristics of the colony images were extracted. Based on gray-level co-occurrence matrix (GLCM), twenty texture features of colony images were obtained. Then, three essential dimension estimation methods, namely, the correlation dimension estimator, the maximum likelihood estimator and the packing numbers estimator, are used to estimate the dimension of the inner low-dimensional structure of the high-dimensional data of the colony images. The results of different dimension estimators were combined with six different dimension reduction techniques (PCA, LDA, MDS, ISOMAP, SNE and NCA) respectively to form 18 different data dimension reduction methods. Based on the distance criterion function, an optimal dimension reduction method for the high-dimensional feature data of the colony images corresponding to the maximum distance criterion function was obtained. © 2021. All Rights Reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Identification and Classification of Rare Medicinal Plants

Identification and Classification of Rare Medicinal Plants

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Piyush Singh Bora Shamneesh Sharma Isha Batra Arun Malik Farzeen Ashfaq School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The identification and conservation of rare medicinal plants are crucial, given their invaluable role in healthcare, pharmaceuticals, and biodiversity preservation. Numerous varieties face extinction according to the International Union for Conservation of Nature (IUCN), necessitating advanced image processing and computer vision techniques for accurate identification and documentation. This paper presents a novel deep learning approach leveraging convolutional neural networks (CNNs) and transfer learning to adapt the MobileNet V2 architecture for automated identification and classification of rare medicinal plants found in different areas, utilizing a curated dataset of 6,900 images across 80 distinct classes. The robust deep learning model extracts discriminative visual features from digital images, facilitating accurate taxonomic classification, and outperforming traditional techniques and shallow models. The proposed system empowers stakeholders with reliable accessible tools and plays a pivotal role in biodiversity conservation and sustainable utilization, ensuring nature's therapeutic bounty for generations.

关键词： Medicinal plants Deep learning Adaptation models Technological innovation Accuracy Medical services Mobile applications Environmental management Biomedical imaging Pharmaceuticals

来源：评论

学校读者我要写书评

暂无评论

StorySpinner: AI Based Technique to Narrate the Web Links

StorySpinner: AI Based Technique to Narrate the Web Links

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Prajwal Kumbar Shamneesh Sharma Isha Batra Arun Malik Farzeen Ashfaq School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The rapid advancement of LLM has facilitated a variety of large language model applications in natural language processing. This report details the development of StorySpinner, an application based on large language models and retrieval-augmented generation that allows yield from articles to the format of an interesting narrative. Moreover, the model addresses processing issues related to the specifics of diverse Internet re-sources, webpages, blogs, and even YouTube videos, by combining web scraping methods, text summarization models, and semantic paragraphs with the use of the extraction of material from extracted abstracts into LLM. The present research investigated the specifics of StorySpinner architecture, the used techniques of information extraction, summarization, and narrative generation, and possible applications of this technology such as educational purposes, accessibility issues, and content development. The paper also provided a discussion of ethical aspects, including bias prevention, copyright issues, and AI technology influence on society. StorySpinner was introduced as a method to fill the gap between web content and human understanding, offering a new way to access information as a story.

关键词： Video on demand Large language models Prevention and mitigation Semantics Text summarization Market research Natural language processing Internet Web sites Videos

来源：评论

学校读者我要写书评

暂无评论

APE-GAN++: An Improved APE-GAN to Eliminate Adversarial Perturbations

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2021年第3期48卷 1-18页

作者： Yang, Rui Chen, Xiu-Qing Cao, Tian-Jie Master's student of the School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu China Lecturer of the School of Medicine Information and Engineering Xuzhou Medical University XuzhouJiangsu China Professor of the School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu China

Deep neural networks (DNNs) have been deployed successfully in various scenarios, but numerous studies have shown that deep neural networks are vulnerable to the attack of adversarial examples. In order to protect deep neural networks against adversarial examples, a lot of countermeasures have been developed. The APE-GAN is one of these proposed countermeasures, which employ a generative adversarial network (GAN) to eliminate adversarial perturbations. Although it performs more excellently than other countermeasures, it still has some shortcomings. First, its training process is precarious and has a vanishing gradient problem. Second, its performance can be improved further. In this paper, we propose the APE-GAN++, which is an enhanced APE-GAN, to overcome its disadvantages. First, the proposed APE-GAN++ utilizes the WGAN-GP loss to make the training process stable. Then, it uses a newly added third-party classification loss to enhance the capacity of the generator to eliminate adversarial perturbations. Experiments are conducted on the MNIST and CIFAR-10 datasets to verify the proposed APE-GAN++'s performance. Experimental results show that the proposed APE-GAN++ has a stable training process and solves the vanishing gradient problem. Besides, it can also achieve a more excellent performance than other countermeasures when defending against adversarial examples. Experimental code is available at https://***/Afreadyang/APE-GAN-Plus-Plus. © 2021. All Rights Reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Fakenews AI: Identifying and Flagging Misinformation through Artificial Intelligence

Fakenews AI: Identifying and Flagging Misinformation through...

引用

Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Aman Kumar Rohan Raj Ajay Sharma Abhishek Kumar Shamneesh Sharma School of Computer Science and Engineering Lovely Professional University Phagwara India Delivery and Student Success upGrad Education Private Limited Banglore Karnataka India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Misinformation has become a pervasive problem in today's society. With the rise of social media and the ease of sharing information, false or misleading information can spread rapidly and have a significant impact on public opinion and decision-making. The challenge of identifying and flagging misinformation has become increasingly difficult, as conventional methods of fact-checking and verification struggle to keep up with the scale and speed of information dissemination. In this study, the author will explore the function of artificial intelligence (AI) in identifying and flagging misinformation, and the potential implications and future directions of the technology, looking at different types of models like Naive Bayes, LR, DT, RF, SVM, and LSTM. The author has tested how well they worked using measures like accuracy, precision, recall, and F1-score. The author has observed that the Decision Tree model is the best one overall. It had the highest accuracy and did the best job at classifying things compared to the other models.

关键词： Radio frequency Support vector machines Accuracy Social networking (online) Decision making Linguistics Decision trees Fake news Long short term memory Random forests

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：