检索结果-内蒙古大学图书馆

3rd International Conference for Advancement in technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

来源：评论

学校读者我要写书评

暂无评论

Advanced Meta-Heuristic Algorithm Based on AlBiruni Earth Radius and Particle Swarm Optimization Methods for Oral Cancer Detection

Advanced Meta-Heuristic Algorithm Based on AlBiruni Earth Ra...

引用

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

作者： Kumar, B.H. Manjunatha Ananya, G.R. Anusha, S.V. Paramanantti, Poornima Prakruthi, K. S J C Institute of Technology Computer Science & Engineering Chikkaballapur India S Jc Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Oral cancer is a widespread and potentially fatal disease, often diagnosed in later stages, leading to poor outcomes and increased mortality rates. Early detection is crucial for improving patient prognosis and survival rates. To address this challenge, a novel method for early oral cancer diagnosis is proposed in this study, utilizing advanced meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and the Al Biruni Earth Radius Algorithm (ABERA). By incorporating PSO and ABERA, the method aims to enhance the accuracy of detection models. Initial testing on standard biomedical imaging datasets shows promising results. This suggests potential for more efficient screening procedures and personalized treatment strategies. However, further research is necessary to validate these findings and explore additional aspects of oral cancer diagnosis and management. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Assessing Document Clustering Algorithms for Effective Information Retrieval 8

Assessing Document Clustering Algorithms for Effective Infor...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Patel, Vaishali Hiran, Dilendra Dangarwala, Kruti Paher University Department of Computer Engineering Rajasthan Udaipur India Institute of Technology Department of Computer Science and Engineering Svm Bharuch India

ISBN: (纸本)9798350376425

An exponential growth of document data on the web, Effective information retrieval is a challenging task which fulfills user's query. Cluster based information retrieval as well as Pattern mining is the most popular solution to retrieve relevant Information queries are based on a user's query on the web. Document clustering is one of the most effective techniques for information retrieval. And many solutions have been proposed for effective and quality. Information retrieval from a set of documents for a specific field of Research. K-Means is one of the widely used document clustering algorithm for information retrieval from sets of documents on web. However, it has major challenging issues which affect on The performance of information retrieval. This work discusses the importance of document clustering for information retrieval, motivation for document clustering and categories of document clustering algorithms. This work also presents steps of proposed work, implementation of traditional clustering algorithms, Result analysis and discussion of variants of K-Means, Mini-BatchkMeans, Spectral using term frequency and dimension Reduction measures on 20 NewsGroups document dataset. It concludes the importance of cluster based information retrieval from a large set of documents on the web, various research progresses, major challenges and future research work direction for effective Information retrieval. © 2024 IEEE.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

Prediction of Insurance Claims for Health Sector using Machine Learning Techniques 3

Prediction of Insurance Claims for Health Sector using Machi...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pragatheeswari, E. Kavitha, M.N. Saranya, S.S. Dhanushree, D. Jasmitha, S. Abiradhie, P.V. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science and Design Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Nowadays, health issues play a tremendous role in day-to-day life and the medical expenditure to get treatment becomes more difficult for the ordinary people. Health insurance has become a vital aspect of people's lives. In this massive community, to access healthcare services such as insurance policies, LIC, ICICI, HDFC ERGO, Star Healthcare are benefits for claiming an amount for their medical expenses. The dataset encompasses three categories, such as generalized data, hospitalized data and claim data. Each record in the dataset represents an individual’s health insurance charges along with corresponding demographic and health-related characteristics. In this paper, Machine Learning techniques are used to group the claim amount that has been adopted by the individuals based on the hospitalized expenditure and insurance charges paid by the individuals. In the health sector, machine learning techniques are used to forecast insurance claims by building predictive models from historical data on claims, patient demographics, treatment types, and results. By predicting future claims based on trends seen in the data, these models help insurance firms better manage risk and allocate resources. In case of a fresher to this concept, it feels challenging to understand whereas generating as a pattern can be more flexible to acquire knowledge about how much amount has been claimed. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

A New Approach to Determine Feasible Operating Region of Unbalanced Distribution Networks With Distributed Photovoltaics

引用

IEEE Transactions on Power Delivery 2025年第3期40卷 1493-1504页

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai400076 India Monash University Department of Electrical and Computer Systems Engineering MelbourneVIC3800 Australia

Amidst rising distributed generation and its potential role in grid management, this article presents a new realistic approach to determine the operational space and flexibility potential of an unbalanced active distribution network. The feasible operating region of an active distribution network is constituted by its range of active-reactive power exchanges with the bulk power system without breaching network constraints. This study explores the potential of highly penetrated single- and three-phase distributed photovoltaics and voltage-sensitive loads to determine the feasible operating region at different operating points. The cumulative response of Volt-Var controlled distributed photovoltaics and voltage-sensitive loads determines the power flow at the bulk power system-active distribution network interconnection, ensuring feasibility with respect to voltage magnitude and unbalance constraints. Further, the correspondence of feasible operating region with the tap-settings of substation transformer enables a novel feature to traverse throughout the available operating region and facilitate bulk power system-active distribution network power coordination. Additionally, a nodal sensitivity-based adjustable Volt-Var control scheme is proposed for distributed photovoltaics, improving the feasible operating region. The proposed strategy is validated on bulk power system-active distribution network integrated test system. Bulk power system: IEEE-9 bus system – Active distribution network: (highly unbalanced IEEE-13 bus distribution system (medium voltage) + modified CIGRE low voltage system). © 1986-2012 IEEE.

关键词： Power distribution networks

来源：评论

学校读者我要写书评

暂无评论

DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Security and Efficiency: Advanced Public Key Cryptographic Solutions for the Modern Era 4

Enhancing IoT Security and Efficiency: Advanced Public Key C...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sekaran, Ramesh Ramasamy, Dhanalakshmi Basha, John Basha Mohamed Maruthapillai, Kumaresan Annamalai, S. Parasuraman, Manikandan Department of Computer Science and Engineering Bangalore India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering-AI Madanapalle India

ISBN: (纸本)9798350387490

The landscape of the Internet of Things is evolving rapidly. The security and efficiency of data handling and device management are multifaceted and complex problems, which are only exacerbated by the huge volumes of data and the sprawling growth of IoT deployments. The goal of this paper is to outline innovative solutions to the most pressing issues in the field through the use of advanced cryptographic protocols and scalable management systems. The integrity and maintainability of IoT ecosystems face threats not only from quantum-capable attackers but also from vendor lock-in, which necessitates special attention. First, the Secure Data Retrieval Encryption system will be presented, which is based on the Learning with Errors assumptions, Self-Managed Public Key Infrastructure, Next-generation Hashed Elliptic Curve Protocol, and Dynamic Unclonable Physical Properties. In a single round of queries, SDRE enables forward security and efficient searches defined by authorized clients. This work presents a distinctive innovation. Initially, the search process was accelerated greatly, and the data being transmitted and decrypted was implemented based on secure computation. In addition, the Self-Managed Public Key Infrastructure protocol is a simple and standard-acquiescent answer for changing control between IoT service providers. SMS-PKI seeks to diminish this necessity of manual effort, especially in light of the massive upsurge in IoT deployments. SM-PKI also mechanizes the IoT PKI credentials updating and trusted domains process, allowing multiple domains to be run on one client apparatus. Tamarin is used to prove security and efficiency, which demonstrates the necessity of using SM-PKI to maintain desired security properties with the minimal cost and device overhead. Finally, a universal authentication scheme grounded on edge computing and involving Next-generation Hashed Elliptic Curve Protocol and Dynamic Unclonable Physical Properties will be shown. This approach

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Bayesian Neural Networks for Satellite Fog Detection: Quantifying Epistemic and Aleatoric Uncertainties

引用

Remote Sensing in Earth Systems Sciences 2024年第1期8卷 232-250页

作者： Deshpande, Prasad Tripathi, Shivam Bhattacharya, Arnab Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur India Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

Conventional threshold-based dual-channel methods, as well as recent deep learning-based methods, can deterministically detect fog using satellite observations. However, stochastic processes like fog are best represented by probabilistic models. This study proposes a method for probabilistic fog detection from satellite data using the Bayesian neural network (BNN)-based model. The novelty of the method lies in quantifying fog detection uncertainty and disentangling its epistemic and aleatoric components. The model generates a probability of fog for each input, which can be thresholded into fog/no-fog status, if needed. Using data from the INSAT-3D geostationary satellite over 18 North Indian cities, we demonstrate that the proposed method significantly outperforms the operational fog detection product of the Indian Space Research Organization, INSAT-FOG. The critical success indices (CSI) of the proposed model during testing and the INSAT-FOG are 0.48 and 0.12, respectively, whereas their probabilities of fog detection (PoD) are 0.68 and 0.13, respectively. The diurnal evaluation of the results shows that the model performs better even during dawn and dusk, which is a critical challenge in satellite fog detection. Moreover, the proposed model can detect very dense fog observations (visibility < 50 m) accurately (with CSI 0.93). On average, the detection uncertainty is lower for correct detections than for incorrect detections, suggesting that the model is more confident about correct detections. The uncertainty analysis reveals that the aleatoric uncertainty is usually higher than the epistemic uncertainty, suggesting potential avenues for future improvements in satellite fog detection. It is concluded that the proposed BNN is a promising method for real-time operational fog detection. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bayesian networks Indo-Gangetic Plains INSAT-3D Reliable AI Smog Visibility

来源：评论

学校读者我要写书评

暂无评论

Improved Software Fault Prediction Model Based on Optimal Features Set and Threshold Values Using Metaheuristic Approach

引用

SN computer Science 2023年第6期4卷 770页

作者： Singh, Manpreet Chhabra, Jitender Kumar Computer Engineering Department National Institute of Technology Kurukshetra India

Software fault prediction models are very important to prioritize software classes for effective testing and efficient use of resources so that the testing process’s time, effort, and cost can be reduced. Fault prediction models can be based on either metrics’ threshold values or machine learning. Code metrics’ threshold-based models are easy to automate and faster than machine learning-based models, which can save significant time in the testing process. ROC, Alves ranking, and VARL are famous threshold value calculation techniques. Out of which ROC is the best threshold calculation technique. This research article proposes a new threshold values calculation technique based on metaheuristics. A genetic algorithm and particle swarm optimizer are used to calculate the threshold values, and the proposed technique is tested on ten open-source object-oriented software datasets and four open-source procedural software datasets. Results show that the metaheuristic-based thresholds give better results than ROC-based thresholds. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： GA Metrics’ threshold PSO ROC Software fault prediction

来源：评论

学校读者我要写书评

暂无评论

Analyzing Sentiments: A Comprehensive Study of Roberta-Based Sentiment Analysis on Twitters 4

Analyzing Sentiments: A Comprehensive Study of Roberta-Based...

引用

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

作者： Krishnamoorthy, A. Sundhar, K.A. Naveen Kumar, V. Karthik, V. Vellore Institute of Technology School of Computer Science & Engineering Tamil Nadu India Vellore Institute of Technology Department of Computer Science Engineering Tamil Nadu India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

Social media platforms have become essential avenues for individuals to express opinions and emotions across diverse topics such as products, events, and policies, using both text and emojis. Understanding these sentiments is crucial, yet manually analyzing the vast data on platforms like Twitter is impractical. Automated sentiment analysis tools, exemplified by Roberta, offer a solution. Roberta, an evolving language model, employs advanced bidirectional learning to predict sentiment labels accurately. This study focuses on sentiment analysis on Twitter, leveraging Roberta to autonomously categorize tweets into positive, negative, or neutral sentiments. We utilizing natural language processing and machine learning algorithms for dataset preprocessing, incorporating sources like Kaggle and ensuring data integrity. Assessment metrics encompassing accuracy, precision, recall, and F1-score, in addition to confusion metrics, assess model performance. According to our findings, machine learning powered sentiment analysis models are dependable, even in the face of challenges and more so when processing Twitter data sets. The study gives direction on how this kind of model can be of help in analyzing sentiments expressed through social networking sites thus assisting scholars and professional Consultancy in the field. © 2024 IEEE.

关键词： Tweets

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：