检索结果-内蒙古大学图书馆

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Gowri, S. Jabez, J. Nirmalrani, V. Srinivasulu, Senduru Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examination of both organized and unstructured medical data. It provides real-time decision assistance and data management by utilizing the scalable, secure, and efficient data processing capabilities made possible by cloud computing. Improved patient care, more effective therapy, and higher-quality healthcare are some areas where Advanced Double Input Layered Neural Network can make a difference. Rapid, precise, and secure data analysis;the administration of multiple data sources;and realtime decision support are a few of the difficulties inherent in medical diagnosis. This research proposes an Advanced Double Input Layered Neural Network (ADILNN) with a double input layered neural community, enabling it to examine prepared and unstructured scientific data. This novel technique improves the community's mastering functionality from various data kinds. By centralizing statistics garage, processing, and analysis on the cloud, computing sources may be extra reliably accessed whilst wanted. The network's diagnostic precision and flexibility are each advanced through way of tool gaining knowledge of strategies (MLM). Because of its adaptability, ADILNN can be utilized in diverse medical fields, which include radiology, pathology, cardiology, and genetics. It permits examine genomic information, making recovery alternatives, and analyzing x-ray photos. The technique has numerous ability makes use of in healthcare, improving prognosis accuracy in diverse settings. Simulation analysis is used to gauge ADILNN's capacity by way of gauging its diagnostic accuracy, processing speed, scalability, and records protection. These research validate ADILNN's potential to improve clinical analysis, streamline facts management, and guarantee healthcare records's safe and effectiv

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Multi-Convolutional Neural Network Based Crowd Counting Using VGG-19 Architecture

Multi-Convolutional Neural Network Based Crowd Counting Usin...

引用

International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： John Pradeep, J. Raja Arumugham, M. Santhi, S. Kalaiselvi, S. Department of Information Technology National Engineering College Kovilpatti India Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9789819746491

Crowd counting has raised a major issue in computer vision with its extensive range of applications, like crowd management, public protection, and city development. The proposed model involves developing a method that can precisely determine the total number of crowds from a raw photograph captured from a random viewpoint and crowd density. A density map-based estimation that uses Multi-convolution Neural Network that is built on the VGG-19 architecture is developed that are trained to categorize the visual inputs with varying densities. The deeper architecture allows VGG-19 to learn more complex features from the input image, which is especially beneficial in crowd counting tasks where the input image contains a huge number of objects. The proposed crowd counting technique combines the VGG-19 architecture with the MCNN model to achieve accurate results. By using filters of different sizes, the MCNN model can handle input images of varying sizes or resolutions, while also accounting for variations in individuals or head sizes caused by viewpoint effects. This allows to accurately estimate crowd density without needing to identify the perspective map of the input image, making it highly versatile and operative for crowd counting tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

引用

IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism Spectrum Disorder Detection

Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism S...

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kavitha Nair, R. Ranjana, P. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350398465

Autism Spectrum Disorder (ASD) is regarded as a spectrum of developmental problems with neurological roots that affect communication, social interaction, and behavior. As it has a detrimental effect on both psychological and physical health, the anxiety experienced by the average individual concerns the physician, especially for those with ASD. Researchers are working very hard to understand the genetic underpinnings of autism and use the genetic information to choose logical targets for efficient therapy. International consortiums are using linkage analysis to pinpoint chromosomes and their correlation with ASD in order to pinpoint potential genetic illnesses. This study applies a recurrent neural network with fuzzy logic and provides an empirical assessment of how well it can diagnose autism using data from an MRI scan of the brain. To evaluate their performances, precision, sensitivity, specificity, and classification accuracy metrics are used. The experimental results show that FRNN, a deep learning-based classifier, produces the best outcome. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Students Grievance and Database 2

Design and Implementation of Students Grievance and Database

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Kuhan, Sakthi Grace, L.K.Joshila Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Tracking student data and complaints is critical to track student performance in the classroom even while studying. This research study aims to address the grievances of the students and presents the website as a portal built using Javascript, HTML, Python, and MySQL, where students may file concerns, and the department handling the case is notified. The student has complete access to the complaint's history and may see if it has been examined, investigated, sent, rejected, or handled. The proposed technique is totally transparent, and if a complaint remains unanswered for several days, the system will automatically transmit it to the person specified further up the hierarchy. Any correctional system should be concerned about language quality since it may be used to spread incorrect information by making comments about people's gender, color, or religion. Employing the state-of-the-art technologies like deep learning and machine learning helps to detect hate speech. After training 11,325 tweets and analyzing the outcomes using evaluation metrics including F1 score, recall, and precision. Bi-LSTM, LSTM, and SVM models are utilized. By reaching the metrics values of 0.884, 0.84, and 0.86, the LSTM model outscores the other models. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Quantified emotion analysis based on design principles of color feature recognition in pictures

引用

Multimedia Tools and Applications 2024年第19期83卷 57243-57267页

作者： Chen, Chin-Ling Huang, Qing-Yang Zhou, Ming Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan School of Information Engineering Changchun Sci-Tech University Jilin Province Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Xiamen Academy of Arts and Design Fuzhou University Fujian Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Fujian Xiamen361024 China Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan

When people observe pictures, different pictures will generate different emotions, and the painters often convey emotional energy to the audience through the media. Through the effect of this emotional transfer, people get emotional satisfaction psychologically, and the author can better express the emotion he needs to express by using the principle. For those who do not know art appreciation, it is difficult to accurately describe the feelings brought by pictures and describe them accurately. It is also difficult to quickly and accurately find the picture of the corresponding feeling sought by oneself. This paper presents a quantitative picture emotion analysis method. Based on the analysis of the characteristics and colors of the pictures that arouse people's emotions, the classification planning is carried out in RGB space and HSV color space respectively. And the combination analysis of the color features with strong influence is carried out to select a more appropriate color space for the expression of human feelings. At the same time, the paper also discusses the effect of the black-and-white filling and the brightness saturation of the image. Finally, it makes use of the corresponding design principles to carry out a quantitative analysis of the emotion generated by it. The system performs a combined analysis of a picture from these directions and presents the results to the user. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Enhancing Trajectory Tracking of Quadrotor Using Feedback Linearization with MPC-LPV and LQI-LPV under Variable Disturbances 3

Enhancing Trajectory Tracking of Quadrotor Using Feedback Li...

引用

3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Chowdhury, Raian Haider Imtiaz Ferdous, Ahmed Hossain, Amreen Rahman, Kazi Arafat Bangladesh University of Engineering and Technology Department of Mechanical Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The deployment of Unmanned Aerial Vehicles (UAVs) has markedly improved industrial efficiency. Optimizing control algorithms for precise path tracking is essential for enhancing the reliability and performance of these systems. This paper presents a comparative analysis of Model Predictive Controller (MPC) and Linear Quadratic Integral (LQI) controller, combined with the Linear Parameter Varying (LPV) approach, for quadrotor trajectory tracking under variable disturbances. The quadrotor is modeled using Newton-Euler equations for a six-degrees-of-freedom system. The control architecture includes an outer loop position controller using Feedback Linearization (FL) and an inner loop attitude controller implemented as either MPC-LPV or LQI-LPV. Disturbances are modeled using a custom sinusoidal model. Numerical simulations are done to evaluate controller performance for six different trajectories. The results for the helical trajectory indicate that under variable disturbances, the MPC-LPV+FL outperforms the LQI-LPV+FL. The MPC-LPV+FL achieves a steady-state error of 0.1934 m compared to 0.6611 m for the LQI-LPV+FL. Additionally, the MPC-LPV+FL reaches the minimum error in 3.1 seconds, compared to 3.8 seconds for the LQI-LPV+FL, resulting in a 22.58% faster response. Therefore, MPC-LPV+FL is the superior choice based on accuracy, stability, and speed of response under adverse conditions. © 2024 IEEE.

关键词： Feedback linearization

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

引用

computer Systems science & engineering 2024年第2期48卷 341-362页

作者： Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq Department of Computer Science and Engineering KoneruLakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the *** Health Organization(WHO)and many others advised controlling Corona Virus Disease in *** limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to *** all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)*** countries made masks mandatory to prevent *** such enforcement,automatic and effective face detection systems are *** study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without *** contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or *** paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection *** experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced *** proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing *** outstanding work also can be used in video surveillance applications.

关键词： Transfer learning depth analysis convolutional neural networks(CNN) COVID-19

来源：评论

学校读者我要写书评

暂无评论

Harnessing Bio-Inspired Optimization and Swarm Intelligence for Energy-Aware TinyML in IoT 7

Harnessing Bio-Inspired Optimization and Swarm Intelligence ...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kalyanakumar, P. Pandian, S. Srinivasa Boopalan, S. Jesintha, D. Kani Krishnan, R. Santhana Muthu, A. Essaki Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

This research investigates the integration of bio-inspired optimization and swarm intelligence principles with TinyML for the development of energy-aware Internet of Things (IoT) devices. A novel model algorithm, termed "BioSwarmML,"is introduced and evaluated against existing algorithms through comprehensive simulation analyses employing suitable metrics. The proposed framework aims to enhance energy efficiency in IoT applications by leveraging the collective intelligence derived from bio-inspired optimization and swarm behaviors. The "BioSwarmML"algorithm is designed to draw inspiration from natural processes, incorporating bio-inspired optimization techniques such as genetic algorithms, simulated annealing, and evolutionary strategies. Concurrently, swarm intelligence principles are integrated to emulate decentralized and self-organizing behaviors observed in biological systems. This amalgamation aims to optimize the energy consumption of TinyML models on IoT devices, facilitating sustainable and adaptive learning processes. Simulation analyses involve a comparative study with established algorithms, evaluating BioSwarmML based on metrics such as energy consumption, accuracy, and latency. The results demonstrate the efficacy of the proposed algorithm in achieving superior energy efficiency while maintaining competitive performance in terms of accuracy and responsiveness. The comparison sheds light on the advantages of BioSwarmML in energy-aware TinyML applications, showcasing its potential for widespread adoption in IoT ecosystems. This research contributes to the advancement of energy-efficient IoT systems by introducing a novel algorithmic paradigm that aligns with international journal standards. The proposed "BioSwarmML"model showcases a promising avenue for enhancing sustainability in TinyML-driven IoT applications, offering a valuable addition to the existing body of knowledge in the field. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Understanding the Emotions Behind Spoken Word Signals With Machine Learning

Understanding the Emotions Behind Spoken Word Signals With M...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Pravin, A. Prem Jacob, T. Sreekrishna, M. Gopika, G.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350305708

Speech analysis has emerged as a crucial tool in bridging the gap between the real and virtual worlds as the amount of human contact with machines increases. One subfield that has long been investigated in both psychiatry & phonology is the identification of affective states in spoken language. The use cases for speech emotion recognition are many. The fundamental objective of this study is to classify feelings from spoken language and categorise them into the following seven emotion output classes: rage, apathy, contempt, fear, happy, sorrow, & neutrality. The suggested method employs the Berlin dataset of heartfelt moment and is predicated on the MFCC & intensity of the acoustic features used as characteristic sources. To train various feature algorithms, such as SVM, RDF, & Gradient Boosting, the characteristics voice data is converted into an input vectors. The greatest rate of correctly anticipated emotions (81.05%) was achieved by Random Forest. © 2023 IEEE.

关键词： CNN RAVDESS Database RF SVM

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：