检索结果-内蒙古大学图书馆

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： George, Angelina Ashik, F. Jose, Alphonsa Kumar, T.M. Mohan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Chemistry Bengaluru India

ISBN: (纸本)9798350359688

This study investigates the usage of water in urban areas, with particular attention to location, age, water quality, and bathing habits. We examined the data using machine learning, more especially a RandomForestClassifier. After accounting for several variables, the study's accuracy rate in predicting the types of baths was 83.63% without SMOTE and reached 90.51% after SMOTE analysis. Our knowledge of urban water consumption is improved by this research, which also shows how flexible machine learning is in identifying intricate links within datasets. The results of this study can help with resource management, urban planning, and environmental initiatives. © 2024 IEEE.

关键词： Bath Type Preferences Dataset Analysis Machine Learning Urban Water Consumption

来源：评论

学校读者我要写书评

暂无评论

Advanced transformer model with fine-grained correlation fusion for Multimodal Emotion analysis 2

Advanced transformer model with fine-grained correlation fus...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Krishnan, Sundara Rajulu Navaneetha Sangeetha, M. Srinivasulu, Senduru Surendran, R. Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331534950

In recent days, Emotion recognition has garnered a lot of attention because of its important applications in human-computer interaction. Human emotion is expressed through a variety of verbal and nonverbal languages, including text, audio, and visual. Current multimodal emotion analysis techniques typically perform poorly because they don't fully utilize the link between various modalities. An advanced transformer model with fine-grained correlation fusion (AT-FGCF) is presented in this research to identify emotion in text, audio, and visual modalities. To enhance the representation of the input audio and video, dense hierarchical feature maps are captured using the Dense Swin Transformer (DSwinT) model. Also, an Emotion- Aware Cognitive BERT (EAC-BERT) is introduced to enhance text representation. Lastly, a fine-grained correlation fusion is carried out to take advantage of correlations between fine-grained features from multimodal data. Our experimental findings show that the suggested method is successful in reaching radical outcomes across two benchmark datasets. That is the datasets IEMOCAP, and MELD all have average accuracy improvements of about 88.35%, and 84.80% respectively. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Survey on Asclepius: A User-Centric Chatbot Bridging the Gap in Healthcare Integrated with VPN Technology 4

Survey on Asclepius: A User-Centric Chatbot Bridging the Gap...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shukla, Garima Sharma, Dolly Awasthi, Vanshaj Shekhawat, Deepika Shukla, Pavan Kumar Saranya, A. Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Maharashtra India Graphic Era Hill University School of Computing Haldwani India Amity University Mumbai Department of Computer Science and Engineering Mumbai India Noida Institure of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350364729

This groundbreaking paper introduces Asclepius, a revolutionary software solution aimed at transforming early healthcare assistance through the seamless integration of cutting-edge technologies. At its core lies a sophisticated chatbot system, augmented with machine learning capabilities, facilitating swift and efficient connections between users and local medical professionals. With a strong emphasis on user anonymity and security, particularly concerning sensitive mental health concerns, Asclepius ensures utmost privacy throughout the interaction process. Constructed with meticulous attention to healthcare technology and security, Asclepius has been developed following extensive research across various domains, incorporating insights from acclaimed research articles to optimize and build an effective model. Central to its functionality is the utilization of state-of-the-art facial recognition technology and secure authentication protocols, instilling confidence in the system's reliability by enabling accurate user verification upon login. As of the publishing date, Asclepius has an 89% accuracy for its diagnoses by making use of a Supervised Decision tree-based Machine Learning Model. Through the incorporation of integrated virtual private network (VPN) technology, the project further establishes a secure communication channel between users and healthcare professionals, guaranteeing the protection of sensitive information and fostering a safe, confidential environment for seeking assistance. By seamlessly integrating a vast array of advanced technologies, Asclepius provides users with a comprehensive and user-friendly interface, prioritizing convenience and security in healthcare interactions and heralding a transformative shift in the landscape of early healthcare assistance. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Quantum-Driven Big Data Processing

Quantum-Driven Big Data Processing

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Handa, Devansh Nikhil, Kindi Krishna Duvarakanath, S. Murali, K. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India Amrita School Of Engineering Amrita Vishwa Vidyapeetham Department Of Mathematics Bengaluru India

ISBN: (纸本)9798350371567

The proposed study realizes a novel quantum machine learning (QML) architecture that allows heuristic function evaluation and can actually perform quantum circuits during massive data processing. The Quantum-Circuit fusion or the Quantum-Data fusion approach to information technology is the latest strategy revolutionizing the major applications with vast computing needs in present time. Our method, which is task - agnostic, is aimed to increase the machine operations speed and scale as the concepts of quantum computing are applied. In this research article, the theoretical background and perspectives are described, so generally, the idea of the combination of quantum circuits is clarified. The research delivery shows as well of this combination as its importance and possibilities, offering a new and cool method in the competitive era of data-driven computational sciences, especially in the field of quantum machine learning. Within classical machine quantum-circuit fusion the potential capability to handle a large data set and obtain the new viewpoints consequently quantum machine learning progress represents a promising route. © 2024 IEEE.

关键词： Timing circuits

来源：评论

学校读者我要写书评

暂无评论

A Clustering Approach on Unveiling Global Development Disparities 5

A Clustering Approach on Unveiling Global Development Dispar...

引用

5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Ankireddypalli, Okesh Arella, Mouhitha Gujjula, Saranya Jayan, Sarada Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India Amrita School of Engineering Department of Mathematics Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331528539

This paper seeks to go further and use clustering, hypothesis testing as well as attribute independence analysis to delve deeper into the world development indicators such as birth rate, infant mortality, business taxes rates etc., in a bid to pinpoint key relationships in the underlying socio-economic environment of the world today. Regional classifications, based on socio- economic development indicators, show that there are groupings of countries with similar development characteristics and this makes it easier to review, and analyze regional disparities. Hypothesis testing allows us to determine if there is indeed a correlation, such as between the business tax rates and Gross domestic product [GDP] growth or spending on health care and life expectancy. Also, the interdependence and the extent of the impact of these factors may be determined by identifying how precisely each component correlates with the others. And comparative analysis of these indicators were performed using the clustering techniques such as K means, hierarchical clustering, Density-Based Spatial Clustering of Applications with Noise [DBSCAN] and Spectral. The silhouette score for the clusters using K means and Hierarchical clustering shows up better analysis of the development status of the countries based on the world development features. © 2024 IEEE.

关键词： Inflation

来源：评论

学校读者我要写书评

暂无评论

NeuroDerm: Pioneering Dermatological Diagnostics with Advanced Neural Networks 7

NeuroDerm: Pioneering Dermatological Diagnostics with Advanc...

引用

7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Anjali, T. Abhishek, S. Remya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350341485

Accurate skin tumor identification and categorization are crucial for quick diagnosis and treatment in dermatological care. In our research, we developed a reliable model that successfully categorizes a wide range of skin tumors, obtaining astounding accuracy rates of 91% with DenseNet and 97% with EfficientNet. This feat highlights the advanced architectures' capacity for synergy. Our dataset includes an extensive collection of skin tumor images that includes ten different categories, providing a comprehensive view of all types of skin tumors. These exceptional accuracy rates were attained via meticulous model training and validation, demonstrating the effectiveness of our methods. Our study makes a substantial contribution to dermatological diagnostics using computers. Both DenseNet and EfficientNet recognize complex characteristics in skin tumor images, allowing accurate classification. High accuracy is acheived by both models, which also make the prediction more versatile. Our study's consequences go beyond the confines of academia. Dermatologists and other medical practitioners may use our method as a powerful diagnostic tool for correctly describing skin growths, eventually improving patient treatment and results. © 2023 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Real-Time Classification of Defects During Automated Valve Testing and Inspection 5th

Real-Time Classification of Defects During Automated Valve T...

引用

5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Shweta Agarwal, Trapty Haripriya Hemlata School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

This paper offers a singular method for actual-time type of defects at some point of computerized valve testing and inspection. Mainly, an image processing gadget is developed to discover and classify various defects which includes cracks, dents, foreign items, and different floor irregularities. The machine includes a high-decision digital camera, LED lighting fixtures, processing unit, and defect reputation algorithm. The image processing system captures the picture and methods it to pick out the illness regions. The gadget then extracts the features from every of the detected regions and classifies them the usage of a guide Vector device. The model is demonstrated with manually received pics, and the outcomes show true overall performance in terms of accuracy and speed. Similarly, the system can alert the consumer whilst a potential disorder is identified and the defects can then be in addition inspected and repaired. This approach has the ability to lessen inspection time, enhance manufacturing efficiency, and reduce charges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lighting fixtures

来源：评论

学校读者我要写书评

暂无评论

Prediction of Fetal Brain Anomalies Using Deep Learning Models 5

Prediction of Fetal Brain Anomalies Using Deep Learning Mode...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Subburaj, Venkateshwari Pandiyarajan, Pandiselvam School of Computing Department of Computer Application Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India

ISBN: (纸本)9798331518097

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Real Time Object Detection using Markov Random Fields: Video Anomaly Detection 5

Real Time Object Detection using Markov Random Fields: Video...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Murali, K. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350361155

This research focuses on the field of video anomaly detection, using the combined strength of the Markov Random Fields (MRFs) and autoencoder systems. The image-segmentation model proposed is based on the use of MRF, which is being used its spatial-temporal abilities to, for one, improve its ability to differentiate abnormalities in video content. The use of MRFs and autoencoders for the integration of the MRFs and autoencoders forms a comprehensive representation of the video data, improving the detection of abnormal activities. A wide range of testing is performed on videos randomly selected from the AVENUE dataset in order to emphasize the effectiveness of the proposed method, which outperforms other approaches. In particular, the approach significantly reduces the computation time of the anomaly detection by 60% when compared to the conventional frame extraction method. This efficiency improvement is an indication of the feasibility of this model in detecting anomalies in practical surveillance applications and has the required attributes that can be used to detect an anomaly in the surveillance system within a short time. The results reveal the potential benefits of the proposed framework for the improvement of the anomaly detection accuracy and operational effectiveness, adding to its practical viability being deployed in surveillance settings. © 2024 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud computing Environment for Efficient Resource Scheduling for Distributed Overloading

引用

Journal of Systems science and Systems engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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：