检索结果-内蒙古大学图书馆

4th IEEE Interdisciplinary conference on Electrics and Computer (INTCEC)

作者： Priyadarshi, Rahul Vikram, Raj Huang, ZeKun Yang, Tiansheng Rathore, Rajkumar Singh Siksha O Anusandhan Deemed Univ Fac Engn & Technol ITER Bhubaneswar 752054 India CV Raman Global Univ Dept Comp Sci & Engn Bhubaneswar 752054 India Shanxi Univ 92 Wucheng Rd Taiyuan Shanxi Peoples R China Univ South Wales Llantwit Rd Pontypridd CF37 1DL M Glam Wales Cardiff Metropolitan Univ Cardiff Sch Technol Llandaff CampusWestern Ave Cardiff CF5 2QS Wales

ISBN: (纸本)9798350349467;9798350349450

Wireless Sensor Networks (WSNs) are essential for tracking environmental and physical variables in a range of applications. One of the biggest challenges in WSN deployment is still achieving excellent coverage while preserving energy. In this work, we provide a unique machine learning-based method to improve WSN coverage efficiency. Our technique minimizes energy consumption and delivers better coverage by dynamically modifying sensor node placement tactics depending on current environmental data. the results of the experiments confirm the efficacy of the suggested methodology and underscore its practicability for implementation in many applications that need extensive coverage in ever-changing settings.

关键词： Wireless Sensor Networks Coverage optimization Machine learning Dynamic Deployment Energy Efficiency

来源：评论

学校读者我要写书评

暂无评论

RAPID AUTOMATIC DETECTION OF COVID-19 IN CHEST CT IMAGES USING VGG16 AND TRANSFER learning 6

RAPID AUTOMATIC DETECTION OF COVID-19 IN CHEST CT IMAGES USI...

引用

ISPRS GeoSpatial conference / Joint 6th Sensors and Models in Photogrammetry and Remote Sensing conference (SMPR) / 4th Geospatial Information Research (GIResearch) conference

作者： Gomroki, M. Shah-Hosseini, R. Hasanlou, M. Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran Iran

this paper presents a deep learning approach for swift detection of COVID-19 in chest CT scan images in order to facilitate treatment planning and reduce the burden on hospital resources and staff workload. the detection procedure starts with a preprocessing step, which involves noise removal and resizing, and the pre-processed images are fed to VGG16, which is a powerful deep learning network for image classification applications. All algorithms have been implemented in Python and the deep learning network has been implemented in Tesorflow using the Keras library. Using VGG16, we have achieved 99% and 92% accuracy for the training and test data, respectively. Considering the accuracy of the method, it can be used for swift clinical detection of COVID-19, which could be of useful and magnificent help to treatment personnel. Also, this method is really helpful for detecting patient and starting treatment as soon as possible and reduces the cost of treatments

关键词： COVID-19 CT scan Deep learning Automatic Detection Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Deep learning Based Automated Question Generation for examination System 4

Deep Learning Based Automated Question Generation for examin...

引用

4th international conference on Artificial Intelligence and Signal Processing

作者： Patil, Atharva Patil, Mandar Bhosale, Chinmay Koppal, Vedant Gurav, Uma KITs Coll Engn Autonomous Dept CSE AIML Kolhapur Maharashtra India KITs Coll Engn Autonomous Dept CSE AIML DS Kolhapur Maharashtra India

ISBN: (纸本)9798350350661;9798350350654

this research presents a novel approach to automating the generation of expert-level questions on specific topics, by leveraging advanced large language models. the proposed technique ensures grammatical as well as semantic accuracy due by utilizing a large language model and employs a KNN classifier to extract relevant context from large number of extensive text embeddings. this method accommodates entire textbooks of related fields within a single learner, eliminating the need for multiple learners across different fields. Pretrained on extensive SQaAD data, the Pegasus model generates questions from concise contexts, offering potential applications in examination institutes, competitive exams like SSC and UPSC, interview rounds and beyond. the system requires fine-tuning to generate specific topic related questions by feeding data specific to the topic only. Traditional question generation methods, such as Cloze or discourse cue-based approaches, may generate questions but needed to be explicitly programmed and they do not learn semantic meaning of the question hence compromising question quality. the limitations of rule-based grammatical methods suggests the necessity for a system that comprehends sentence logic and semantics. To address this, the research combines the Universal Sentence Encoder, K-Nearest Neighbor (KNN) Classifier, and Pegasus model. the Universal Sentence Encoder converts sentences into vectors, preserving semantic meaning, and allowing machine learning algorithms to process textual data. the KNN classifier fetches context similar to user-defined keywords, ensuring grammatically and semantically correct paragraphs. Pegasus, using on KNN-derived context, generates grammatically questions, enabling scalable and reliable question generation across diverse topics and fields

关键词： Transformers Word Embedding Deep learning Natural Language Processing Machine learning Text Extraction

来源：评论

学校读者我要写书评

暂无评论

Deep learning Radar Point Cloud Input Improvement Method 4

Deep Learning Radar Point Cloud Input Improvement Method

引用

4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Wan, Qingmian Peng, Hongli Su, Shixian Liao, Xing Liu, Kuayue Li, Hanchen Shanghai Jiao Tong University State Key Laboratory of Radio Frequency Heterogeneous Integration Shanghai China

ISBN: (纸本)9798350361643

Radar point cloud is used as the basic input data for deep learning, in order to improve the accuracy of radar data output, this paper will present the point cloud optimization algorithm for high-precision 4D radar, using the dynamic adjustment of spatial area echoes, and proposes a point cloud generation optimization method. At the same time, in the subsequent AOA angle resolution, echo space positioning can be further integrated with further matching optimization according to downstream needs. Finally, our detection data collected for the 4D radar in real road scenarios and results show that this 4D radar has significant performance improvements in terms of clutter suppression and density generation. It is known that the signal echo threshold is the product of spatial inversion after judgment. therefore, in order to obtain a more accurate multi-channel the point cloud echo signal, the target detection of the echo needs to be optimized first. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Study on the classification of villages in Jilin Province based on space syntax and machine learning

引用

ARCHITECTURAL SCIENCE REVIEW 2024年第5期67卷 465-480页

作者： Liu, Deli Wang, Keqi Jilin Jianzhu Univ Sch Architectural & Urban Planning Changchun Peoples R China 5088 Xincheng St Changchun Jilin Peoples R China

this paper employs machine learning and space syntax to classify villages, revealing implicit formation and development patterns in their spatial layouts. the study addresses village classification in Jilin Province from micro and macro perspectives. At the macro level, we present the classification method for villages based on geospatial distribution features (CVGD).We analyze the geographic distribution of villages and utilize village density for classification, effectively portraying the spatial distribution characteristics of villages. At the micro level, we propose the classification method for villages based on spatial structural features (CVSS). We use space syntax to extract spatial features of villages. Representative features chosen through comparative analysis serve as input variables in clustering algorithms, classifying villages based on high-dimensional data to explore their spatial traits. the paper summarizes village spatial characteristics from different classifications and studies how geographic factors affect village structure. It aims to offer potentially valuable theoretical insights for rural ***: POI: Point of Interest;DBSCAN: Density-Based Spatial Clustering of applications with Noise;OPTICS: Ordering Points to Identify the Clustering Structure;WGS-84: World Geodetic System;ID: Identity Document;SSE: Sum of Squared Errors;CVGD: Classification Method for Villages Based on Geospatial Distribution Features;CVSS: Classification Method for Villages Based on Spatial Structure Features;GIS: Geographic Information Systems;ISUF: international Seminar on Urban Form;LiDAR: Light Detection and Ranging

关键词： Village classification spatial characteristics space syntax machine learning

来源：评论

学校读者我要写书评

暂无评论

Semantic Text Matching Using Intelligent Methods: A Survey 4

Semantic Text Matching Using Intelligent Methods: A Survey

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Alqasemi, Fand Ahmed, Yousef Khaled Aldafer, Mohammed Faris Assarwie, Nooruddine F. Univ Sci & Technol Informat Technol Dept Aden Yemen

ISBN: (纸本)9798350354140;9798350354133

Nowadays, Artificial intelligence (AI) is emerged strongly. AI applications are the future of computing, with numerous services and solutions. Machine learning (ML) and deep learning (DL) are the most important components of AIemerged tools. Natural language processing (NLP) is that field related to incorporating AI tools for improving machine recognition of human natural languages. Text mining and information retrieval applications have become important in frequent intelligent business services. Semantic text matching (STM) is an important application of NLP and text mining. It produces an understanding of two or more text segments in terms of similarity and distance evaluation. In this survey, AI-based STM approaches that are introduced in the literature are reviewed. We present the importance of the STM field using intelligent methods, in addition to explaining the main STMbased concepts. We discuss various categories of STM that appear in surveyed papers, they are the type of language, the deep learning utilized models, question-answering interesting studies, and other smart techniques that were implemented for STM. Finally, we summarize this review by introducing an explanation of some recent papers, in addition to illustrating a comparison between them. the utilized dataset, implemented model, and resulted accuracy were the main points of that comparison.

来源：评论

学校读者我要写书评

暂无评论

QEFMLA-FIDO: Quantum Evolution Formulation for ML Advancement: Fidelity-Improved Data optimization in Quantum-Classical Systems 4

QEFMLA-FIDO: Quantum Evolution Formulation for ML Advancemen...

引用

4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Kundu, Sourodeep Mishra, Deepti Tarasia, Nachiketa Barik, Rabindra Kumar School of Computer Engineering KIIT Deemed to Be University Bhubaneshwar India School of Economics and Commerce KIIT Deemed to Be University Bhubaneshwar India School of Computer Application KIIT Deemed to Be University Bhubaneshwar India

ISBN: (纸本)9798331528171

Using machine learning to improve quantum algorithms or quantum mechanics to improve machine learning is known as quantum machine learning. In order to include quantum physics into machine learning, we provide a unique model (QEFMLA-FIDO) in this research. the way an artificial neural network simulates the human brain is comparable to this method. Our suggested quantum-inspired technique can accelerate computationally challenging issues when executed on traditional hardware. Additionally, the method may easily be modified to operate on quantum technology, offering a far faster speedup than is achievable on conventional hardware. the Quantum Evolution Algorithm (QEA) improves computing efficiency by operating on both classical and quantum hardware and is motivated by the general development of quantum states. the technique solves complicated problems more quickly than conventional methods by iteratively evolving a system's Hamil-tonian to suit training data. It effectively enhances parameters by optimising the Quantum Fisher Information Matrix (QFIM). the QEA is suitable in real-world situations because it uses quantum parallelism to minimise computing complexity and is noise- resistant. It also has a predictive function that can detect improper outputs before they happen. © 2024 IEEE.

关键词： Hamiltonians

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Evolutionary algorithms for Hardware Design Space Exploration 4

Multi-Objective Evolutionary Algorithms for Hardware Design ...

引用

4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Kumar, Rahul Kapoor, Monit Varshney, Neeraj Shrivastava, Anurag Saraswat, Manish Singh, Devendra Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Research Impact and Outreach Punjab Rajpura140401 India GLA University Department of Computer Engineering and Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Lloyd Institute of Engineering & Technology Greater Noida India Uttaranchal University Uttaranchal Institute of Technology Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350307757

this work investigates the use of Multi-Objective Evolutionary algorithms (MOEAs) in the Hardware Design Space Exploration context. through the use of a heterogeneous data set that spanned different hardware configurations and performance measurements, four widely known MOEAs namely NSGA-II, SPEA2 IBEA MOPSO have been systematically assessed. Experiments have been aimed at major indicators, including convergence, diversity, and hypervolume maximization. It has been shown that NSGA-II and SPEA2 have been largely well-balanced in terms of convergence versus diversity, a feature that makes them suitable options for the optimization task. IBEA has been best in hypervolume maximization, which indicates that it is a powerful method when applied to scenarios where extensive coverage of the design space needs to be achieved. MOPSO, using the ideas from particle swarm optimization, gave a solid competitor that featured excellent convergence and diversity. this work also does a comparative study with related works, which shows the generality of MOEAs in different domains such as facility location optimization, system design and control theory robotics bioinformatics computer vision, and networked systems. the research provides relevant inputs to help researchers and practitioners choose the best algorithms for hardware design optimization, which includes considering specific goals and constraints. Consequently, the research shows us how this way and a method of creation. © 2024 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

4th International Conference on Innovations in Computational...

引用

4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

ISBN: (纸本)9789819769919

the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: Dataset Balancing Techniques and Supervised learning algorithms for Predictive Analysis of Rice and Corn Yields;fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence;predictive Analysis for Early Detection of Breast Cancer through Artificial Intelligence algorithms;boosting Security: An Effective Approach to Intrusion Detection in Wireless Sensor Networks with AdaBoost Classifiers;Chat2Fluency: Enhancing Language learning through Conversational AI;REED-NET: Residual Enhanced Encoder-Decoder Network for Low-Dose CT Reconstruction;comparative Analysis of Large Language Models;Visualizing Insights to Empower HR Decision-Making: A Data-Driven Approach;handling Uncertainty in Parkinson’s Disease Voice Data Using Intuitionistic Fuzzy Entropy Measure;Exploring Shopping Opportunities and Elevating Customer Experiences through AI-Powered E-Commerce Strategies;ioT-Based Vehicle Class Detection for Smart Traffic Control;Domain Adaptation for NER Using mBERT;SQL Query Recommendation Based on Matrix Factorization;retenSure: Ensemble learning for Managing Employee Attrition;Subject–Verb Agreement Error Handling Using RNN Architectures;ad-Spend Analytics;an Efficient Real-Time Word-Level Recognition of Indian Sign Language;innovating Drug Design for Alzheimer’s Disease via Reinforcement learning for Enhanced Molecular Generation;an Efficient Real-Time Recognition of Static Kannada Sign Language;unveiling Diagnostic Clarity: A Machine learning Approach to Distinguish Borderline Personality Disorder and Bipolar Disorder for Enhanced Mental Health Diagnostics;DLSTM with Adam Waterwheel optimization for Groundwater Level Prediction in India;PhishGuard: Machine learning Model for Real-Time URL Detection;Improved Grasshopper optimization with Squeezenet (IGO-SNet)

关键词：

来源：评论

学校读者我要写书评

暂无评论

SCIHYPO - A Deep learning Framework for Data-Driven Scientific Hypothesis Generation from Extensive Literature Analysis 4

SCIHYPO - A Deep Learning Framework for Data-Driven Scientif...

引用

4th international conference on Expert Clouds and applications, ICOECA 2024

作者： Tadiparthi, Mothilal Pasupuleti, Sindhu Sathwika Talluri, Sita Venkata Sangaraju, Subramanyam Raju Mogili, Manikanta Gudlavalleru Engineering College Dept. of AI&DS SR Gudlavalleru India

ISBN: (纸本)9798350385793

Scientific progress relies heavily on hypothesis generation, yet conventional methods often rely on expert intuition, limiting exploration of new ideas. To address this, we introduce SciHypo, a deep learning framework leveraging scientific literature to generate fresh hypotheses across diverse fields. SciHypo employs advanced natural language processing, including Generative Pre-trained Transformer to analyze patterns, relationships, and gaps in research. this allows the model to uncover latent connections and propose novel research trajectories. Filter search algorithms ensure the production of relevant hypotheses. A key strength is SciHypo's adaptability across scientific disciplines, enabling transdisciplinary hypothesis generation. Results demonstrate its ability to propose hypotheses resonating with experts, advancing scientific frontiers. © 2024 IEEE.

关键词： Adversarial machine learning

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：