检索结果-内蒙古大学图书馆

Smart question answering system using vectorization approach and statistical scoring method

Materials Today: Proceedings 2023年 80卷 3719-3725页

作者： Manjunath, T.N. Yogish, Deepa Mahalakshmi, S. Yogish, H.K. Information Science & Engineering B M S Institute of Technology &Management Karnataka Bangalore560064 India K S School of Engineering & Management Computer Science & Engineering Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of Master of Computer Applications M S Ramaiah Institute of Technology Karnataka Bangalore560054 India

In this digital era, internet users are increasing day by day to extract information related to various fields such as health, economic, educational, politics and daily needs from Information Retrieval Systems (IRS) such as Google, Ask, Yahoo etc. Information Retrieval (IR) is a conventional method which depends on metadata searching method that is searching of adocuments for information within documents and information about documents. Lot of research in the IR area improves accessing the quality of information. Most of the search engines such as Google, Yahoo, Ask Jeeves depends on keyword based query processing mechanism internally which can't handle natural language questions which results in returning long list of documents for a query. The task of users is to manually explore each document to retrieve correct answer which is a time consuming process. To overcome the problems of conventional search engines, smart question answering system comes to play. The objective of Smart Automated Answering system is to impart accurate and valid answers to user question in natural language rather than set of full documents. The Smart Question answering system which accepts user question in Natural language in specific domain and provide short and precise answer using Natural language processing technique, used for pre-processing, normalized term weighting-TF-IDF with cosine similarity approach for document retrieval and enhanced BM25 ranking function to extract answer. The main focus is on decreasing the response time, designing ranking function and giving the pertinent responses to the users’ intent. The system achieved an overall Precision of 93.2%, Recall of 84.3% and F-measure of 88.5% with 80% of Accuracy. © 2021

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

VoiceSense: Designing A Robust Emotion Detection System 7

VoiceSense: Designing A Robust Emotion Detection System

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mishra, Mihir Mishra, Reeta Pandey, Anjali Mintu Ishaan Gopal Department of Computer Science & Engineering Delhi Technical Campus UP Greater Noida India Iilm University School of Computer Science & Engineering UP Greater Noida India Manav Rachna International Institute of Research & studies School of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350350067

Detecting emotions from speech is an excitingarea of research that can improve how we interact with computers and understand mental health. In our study, we explore how to build a system that can understand different emotions using speech and psychological information. We gather data from various sources like TESS, Crema-D, RAVDESS, SAVEE, and our own custom dataset to create a diverse collection of emotions. To build our system, we use different types of neural networks like CNNs, RNNs, and transformers. After testing our system extensively, we find that it can accurately detect emotions about 77% of the time, which is pretty good. We also look into how our system learns about emotions, which can help us better understand how humans process emotions. Our work not only improves emotion detection technology but also has important implications for making computers more empathetic in how they interact with people. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Temperature and Soil Parameter Monitoring System

Temperature and Soil Parameter Monitoring System

引用

International Conference on Interdisciplinary Approaches in Civil engineering for Sustainable Development, IACESD 2023

作者： Sharma, Pallavi U Raj, Niranjan Shailaja, K.P. Pushpa, T.S. Department of Electronics and Telecommunication B.M.S. College of Engineering Bengaluru India Department of Computer Applications B.M.S. College of Engineering Bengaluru India

ISBN: (纸本)9789819700714

The importance of precise temperature and humidity readings in a wide range of industries and scientific fields cannot be emphasized. They serve as the foundation for quality control, shelf-life evaluation, and yield optimization in industries like food, medicine, and agriculture. Real-time monitoring has entered a new era with the introduction of the ESP32 microcontroller technology, giving previously unheard-of capabilities with broad ramifications. Real-time data gathering and analysis are now possible because of the ESP32’s capability, enabling farmers to make wise decisions based on the most recent information about their crops and soil conditions. Because of this, they can quickly modify irrigation and fertilizer application, maximizing resource use and raising yields. Meanwhile, civil engineers use ESP32’s capabilities to improve construction projects’ efficiency, safety, and teamwork. The ESP32’s ability to integrate temperature and soil monitoring systems is a notable development in this area. It has the extraordinary ability to alter sectors by giving real- time, actionable knowledge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality control

来源：评论

学校读者我要写书评

暂无评论

CCTG-NET: Contextualized Convolutional Transformer-GRU Network for speech emotion recognition

引用

International Journal of Speech Technology 2023年第4期26卷 1099-1116页

作者： Tellai, Mohammed Mao, Qirong Department of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Province Zhenjiang212013 China

Speech is a crucial aspect of human-to-human interactions and plays a fundamental role in the advancement of human–computer interaction (HCI) systems. Developing an accurate speech emotion recognition (SER) system for human conversations poses a critical yet challenging task. Existing state-of-the-art (SOTA) research in SER primarily focuses on modeling vocal information within individual conversational speech utterances, overlooking the significance of incorporating transactional information from the interaction context. In this paper, we present a novel Contextualized Convolutional Transformer-GRU Network (CCTG-Net) for recognizing speech emotions using Mel-spectrogram features, effectively integrating contextual information for emotion recognition. Our experiments are conducted on the widely-used emotional benchmark dataset, IEMOCAP. Compared to SOTA methods in four-class emotion recognition, our proposed model achieves a weighted accuracy of 88.4% and an unweighted accuracy (UA) of 89.1%. This marks a substantial 3.0% enhancement in UA while maintaining an optimal balance between performance and complexity. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Bioacoustics Signal Authentication for E-Medical Records Using Blockchain

Bioacoustics Signal Authentication for E-Medical Records Usi...

引用

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

作者： Sowjanya, S. Reddy, Induru Sunnihith Muralikrishna, Cheruku Prasad, Thota Sai Lalith Reddy, Pundru Chandra Shaker Sharma, Vikas Vardhaman College of Engineering Computer Science and Engineering Telangana Hyderabad India Rmk Engineering College Computer Science and Business Systems Tamilnadu Tiruvallur India Telangana Hyderabad India Teegala Krishna Reddy Engineering College Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Iimt University School of Computer Science & Applications Uttar Pradesh Meerut India

ISBN: (纸本)9798350359688

Many are wary of storing and processing data in the cloud because of the prevalence of hostile assaults on mobile and wireless communication networks, which raises serious privacy and security concerns. Using Blockchain as an example, this article investigates the feasibility of developing a trustworthy decentralized authentication system. Utilizing a blockchain technique to enhance the heftiness of multiple data checks and an optimized number of secured features from the bioacoustics signal, in place of traditional biometric features, ensure high security for the bioacoustics signal authentication mechanism. Verified authentication and monitoring of terminal activities are both made possible by it. Then, to provide security at every terminal and edge node, lightweight cryptography (LWC) is created. Lastly, the bastion of the catching approach is the belief-propagation(BP) strategy, which retrains the bioacoustics signal's attributes. The hit ratio is improved and the delay time is decreased. With a blockchain paradigm for data openness, the investigational setup utilizes bioacoustics signals for authentication instead of standard biometric features, and the efficiency of numerous checks is improved. Both security and privacy are enhanced when this occurs. The association between MFCC and LPCC was 0.9517. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

引用

Neural Computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

来源：评论

学校读者我要写书评

暂无评论

Object Detection in Real-Time Surveillance Using Deep Learning-Based YOLO Framework

Object Detection in Real-Time Surveillance Using Deep Learni...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Praveen, R.V.S. Bansal, Saloni Raju, Hemanth Singh, Navdeep Begum, Rowsonara Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India LTIMindtree Limited Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

The continual growth of the computer vision and artificial intelligence fields overtime have provided the basis for more efficient and accurate surveillance. This paper examines the use of YOLO, a deep learning model that is used in the detection of objects, for use in real-time surveillance systems. The added benefits of identifying and labeling multiple objects in a single pass forward makes YOLO ideal for real-time surveillance. Unlike other approaches to object detection such as region-based methods or methods with multiple stages of detection, YOLO cast object detection as a regression problem where boxes and class probabilities are predicted jointly. This leads to a far improved performance when compared with their slower counterparts without the sacrifice of precision. In this work, the YOLO model is finetuned on a large database of surveillance videos to improve object detection at low lighting and in the presence of occlusion and crowding. The developed system has a high detection speed and accuracy;the application areas will include uses such as security inspection, traffic regulation, and anomaly detection. This is done to affirm that using YOLO is more efficient and faster than the other object detection algorithms in real-time surveillance tasks. The results indicate directions for further improvements to guarantee public safety and improve efficient surveillance systems. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Exploring the Integration of Generative AI in Modern Education Systems: A Comprehensive Analysis

Exploring the Integration of Generative AI in Modern Educati...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Dey, Avick Kumar Chauhan, Vineet Kumar Singh, Pradeep Kumar Sarkar, Ananya Dept. of Computer Applications West Bengal Durgapur India Galgotias College of Engg. & Tech Dept. of Information Technology Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India DSMS College of Tourism and Management Dept. of Computer Applications West Bengal India

ISBN: (纸本)9798331517953

This paper aims at filling this gap through dissecting the applicability of the Generative AI in the current learning and teaching system. The paper offers an introduction to Generative AI technologies: GPT and DALL-E, and describes the chronological history and current progress of its educational applications. Other objectives include discussing about the uses of Generative AI in adaptive learning environment, smart learning systems, automation of organizational work and in talented minds improvement. Analyzing key successful AI cases, the paper reveals the challenges implementers encountered, and provides debates about the appropriate practices of AI. Research evidence points to the prospect of Generative AI benefiting students and educators in teaching and learning, which indicates the innovative potential of the technology, yet at the same time reveals the potential risks and challenges that need to be addressed concerning the ethical and privacy implications of Generative AI in education. Others may follow general recommendations for policymakers and educators on how to integrate artificial intelligence responsibly, as well as future work for this quickly burgeoning domain. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive analysis of 5G Communication and Its Health Care Management Models 2

A Comprehensive analysis of 5G Communication and Its Health ...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar Sagar, Lalit Goel, Dipti Gupta, Sonali Saraswat, Manish Sharma, Ashish Srm Institute of Science and Technology Delhi Ncr Campus Department of Computer Applications UP Modinagar India Raj Kumar Goel Institute of Technology Department of Information Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Gla University UP Mathura India Gla University Department of Computer Engineering & Applications UP Mathura India

ISBN: (纸本)9798350371055

This paper provides an in-intensity examination of 5G conversation and its health care management models. 5G era, the imminent 5th technology of cellular cell communications, is expected to revolutionize the cellular enterprise with its build-up of excessive velocity, low latency, and big capability. however, it also increases new demanding situations for healthcaredelivery, in addition to data protection, privateness and tool management. This paper will are seeking for to deal with those troubles by using exploring numerous 5G fitness care management fashions. extra mainly, this paper will discover the demanding situations of 5G in healthcare, how the current healthcare infrastructure can facilitate 5G's use, and the principle elements of numerous 5G healthcare management fashions. finally, this paper will discuss the destiny implications of 5G in healthcare, and what groups want to do in order to make certain the most green and cozy healthcare shipping. by means of delving into this rely, this paper will provide a complete view of 5G conversation and its healthcare control fashions, main to progressed healthcare delivery within the close to future. This paper presents a complete analysis of fifth-technology (5G) communique applied to healthcare control fashions. To make sure functionality and efficacy., 5G networks and their related services need to adhere to a strict set of requirements and requirements, which can be evaluated in regard to latency, community useful resource requirements, and safety protocols. several important additives and capabilities of 5G-enabled healthcare models are mentioned, which include far off patient monitoring offerings, personalized healthcare services, and pervasive healthcare get right of entry to. additionally, this paper discusses the measures for successful implementation of 5G technology into healthcare, in conjunction with some potential dangers and ethical issues that have to be taken under consideration. in the end, the pap

关键词： cellular demanding models revolutionize

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：