检索结果-内蒙古大学图书馆

Optimization of machining parameters in EDM using Taguchi based grey relational analysis

Materials Today: Proceedings 2022年 82卷 43-46页

作者： Ganapathy S. Palanivendhan M. Susitra K. Balasubramanian P. Department of Mechanical Engineering Sri Sai Ram Engineering College Chennai 600044 India Centre for Automotive Materials Assistant Professor Department of Automobile Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu 603203 India Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai 600044 India Department of Mechanical Engineering A.V.C College of Engineering Mayiladuthurai 609305 India

The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade. © 2023 Elsevier Ltd. All rights reserved.

关键词： Anova EDM EN-31 GRA MRR

来源：评论

学校读者我要写书评

暂无评论

Prediction of Stroke Risk with LSTM Networks and Patient Health Records

Prediction of Stroke Risk with LSTM Networks and Patient Hea...

引用

Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Madhavi Kshatri Wilson Lukose Prasanta Kumar Parida Jalaluddin Khan Department of CSE Raipur Institute of Technology Raipur Professor & Principal Bharati Vidyapeeth Institute Of Hotel Management and Catering Technology Bharati Vidyapeeth (Deemed to be University) Off Campus Navi Mumbai KIIT University Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

This study is the first to use Long Short-Term Memory (LSTM) networks to predict the probability of stroke from patient medical records. Researchers create a complete model that is capable of capturing complex temporal patterns by adopting an interpretivist mindset and a deductive methodology. Analysis in comparison to traditional models demonstrates the LSTM's greater prediction skills. The model's dependability is confirmed by robustness tests along with sensitivity studies. Enhancing interpretability as well as prospective clinical confirmation should be the main goals of future effort. There is room for improvement in the integration of immediate monitoring using multimodal data. This groundbreaking method represents a considerable advance in the timely and individualized prevention of stroke

关键词：

来源：评论

学校读者我要写书评

暂无评论

Empowering Job Seekers by a Natural Language Solution for Personalized Job Descriptions and Enhanced Employment Prospects through Clustering Techniques

Empowering Job Seekers by a Natural Language Solution for Pe...

引用

Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Upinder Kaur Salil Bharany Satvik Vats Anand Kumar Keshav Kaushik School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun India Amity School of Engineering and Technology Amity University Mohali Punjab India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This Paper is based on our project which aims at implementing a Natural Language based solution which would deliver job descriptions based on a few criteria like preparing the transformed dataset, performing NLP processing to rank the best features in the Job Description, training transformer model and implementing an API endpoint which can be consumed over the web. The input data has been refined and data dictionaries have been created to generate Job description based on job title and skills. To anticipate the token that should come after the input, we have trained the GPT2 decoder transformer model to use the most recent token embedding of the input sequence. The study concludes that it is critical for job searchers to customize job descriptions according to an individual's talents, prior experiences, and educational background. While most of the research to date has focused on the client side of the hiring process, the suggested model seeks to empower job searchers by giving them customized job descriptions, which increases the likelihood that they will find acceptable employment. To provide insights into a variety of disciplines and help job searchers better grasp the dynamics of the recruiting process, the initiative made use of an extensive dataset. K-means clustering and DBScan clustering were two of the models and hyperparameters investigated within the project's time restrictions. K-means clustering improved context knowledge, albeit at the expense of adding noise to the data. Subsequent developments may entail investigating topic modeling and clustering strategies in greater detail to strengthen the model. Prospective directions for further research encompass utilizing Part of Speech (POS) tagging to extract distinct portions of job descriptions, implementing machine learning techniques to enhance synonym lists, and investigating substitute text creation models if preliminary efforts provide inconclusive results. With these developments, the suggested mod

关键词： Noise Employment Tagging Speech enhancement Transformers Data models Natural language processing Time factors Recruitment Qualifications

来源：评论

学校读者我要写书评

暂无评论

SparseGTN: Human Trajectory Forecasting with Sparsely Represented Scene and Incomplete Trajectories

SparseGTN: Human Trajectory Forecasting with Sparsely Repres...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jianbang Liu Guangyang Li Xinyu Mao Fei Meng Jie Mei Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen Key Laboratory of Robotics Perception and Intelligence and the Department of Electronic and Electrical Engineering Southern University of Science and Technology in Shenzhen China Professor Emeritus in the Department of Electronic Engineering The Chinese University of Hong Kong in Hong Kong Professor in the Department of Electrical and Computer Engineering University of Alberta in Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, great progress has been made in forecasting human motion in crowded scenes. However, current methods are far from practical applications due to the unbearable high computation costs, especially for encoding scene context. In addition, neglecting the partially detected trajectories makes the predicted outcome deviate from the real trajectory distribution. To handle the aforementioned concerns, we propose to represent the scene context and partially observed trajectories with sparse graphs. Customized for this special data structure, we design a hierarchical Graph Transformer Network model SparseGTN to predict multiple possible future trajectories of the target pedestrian by digesting the sparsely represented inputs. Our approach exhibits superiority over the state-of-the-art (SOTA) methods, utilizing a mere 3.42% of the number of floating point operations (FLOPs) and 0.53% of the number of model parameters. The code will be available online ⋆ .

关键词： Pedestrians Switches Predictive models Transformers Encoding Trajectory Mobile robots Forecasting Collision avoidance Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

SSRN

引用

SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

来源：评论

学校读者我要写书评

暂无评论

A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning

A Novel Forward Filter Feature Selection Algorithm Based on ...

引用

International Conference on Advanced computer Control, ICACC

作者： M A Anitha K K Sherly Assistant Professor of Computer Science College of Engineering Cherthala Kerala India Associate Professor of Information Technology Rajagiri School of Engineering & Technology Kerala India

ISBN: (纸本)9781665439206

In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.

关键词： Machine learning algorithms Redundancy Filtering algorithms Feature extraction Information filters Minimization Filtering theory

来源：评论

学校读者我要写书评

暂无评论

The 27th international conference on case-based reasoning

The 27th international conference on case-based reasoning

引用

作者： Althoff, Klaus-Dieter Bach, Kerstin Bergmann, Ralph Marling, Cindy Institute of Computer Science University of Hildesheim Hildesheim Germany Department of Computer Science Norwegian University of Science and Technology Trondheim Norway University Professor for Business Information Systems University of Trier Trier Germany School of Electrical Engineering and Computer Science Ohio University AthensOH United States

来源：评论

学校读者我要写书评

暂无评论

Rank-based Decomposable Losses in Machine Learning: A Survey

arXiv

引用

arXiv 2022年

作者： Hu, Shu Wang, Xin Lyu, Siwei The Department of Computer Information and Graphics Technology Purdue School of Engineering and Technology Indiana University-Purdue University IndianapolisIN46202 United States The Heinz College of Information Systems and Public Policy Carnegie Mellon University PittsburghPA15213 United States The Department of Computer Science and Engineering University at Buffalo SUNY BuffaloNY14260 United States

Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses vs. aggregate losses. The individual loss measures the quality of the model on a sample, while the aggregate loss combines individual losses/scores over each training sample. Both have a common procedure that aggregates a set of individual values to a single numerical value. The ranking order reflects the most fundamental relation among individual values in designing losses. In addition, decomposability, in which a loss can be decomposed into an ensemble of individual terms, becomes a significant property of organizing losses/scores. This survey provides a systematic and comprehensive review of rank-based decomposable losses in machine learning. Specifically, we provide a new taxonomy of loss functions that follows the perspectives of aggregate loss and individual loss. We identify the aggregator to form such losses, which are examples of set functions. We organize the rank-based decomposable losses into eight categories. Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses. We describe general formulas for these losses and connect them with existing research topics. We also suggest future research directions spanning unexplored, remaining, and emerging issues in rank-based decomposable losses. Copyright © 2022, The Authors. All rights reserved.

关键词： Aggregates

来源：评论

学校读者我要写书评

暂无评论

AI-Based Cybersecurity Frameworks for 7G-Enabled Virtual Therapy Platforms

Cyber Security and Applications

引用

Cyber Security and Applications 2025年

作者： A. Jayanthiladevi Jayapandian Natarajan KP Arjun L. Godlin Atlas M. Arvindhan Daniel Arockiam Professor Department of Computer Engineering Marwadi University Rajkot Gujarat India Associate Professor Department of Computer Science and Engineering School of Engineering and Technology Christ University Kengeri Campus Bangalore Karnataka India Department of Computer Science and Engineering GITAM University Bengaluru Campus Karnataka India Associate Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu Assistant Professor Amity School of Engineering & Technology Computer Science and Engineering Gwalior Madhya Pradesh India Associate Professor CSE Amity University Madhya Pradesh Gwalior 474005 India

The modern technology network plays an important role. Artificial intelligence to provide a higher solution for network security. As 7G networks emerge, virtual therapy platforms have begun to take center stage, offering fast, real-time interaction and high-definition immersive experiences. While the convergence of security in health care and mobility is in many ways even more integrated into our lives, it is fraught with cybersecurity risks. This article proposes an AI-based cybersecurity framework for a 7 G-based virtual therapy platform. The framework employs advanced machine learning (ML) algorithms, predictive analytics, and adaptive threat intelligence to protect the confidentiality, integrity, and availability of data. The article covers the core building blocks like anomaly detection, secure 7 G-based virtual therapy platform authentication protocols, and privacy-preserving techniques. AI-GN gives an overview of attack mitigation using AI with a comparative analysis of existing 5G/6G frameworks and solutions and their unique aspects needed for 7G environments. This article mainly focuses on threat detection using a deep learning method, also it aims to provide a continuous authentication process. The proposed model response time is decreased by nearly 30 percent, at the same time detection rate is increased to 98.5 percent in a 7G network.

关键词： AI-based cyber security framework Adaptive threat intelligence Anomaly detection Predictive analytics Threat detection

来源：评论

学校读者我要写书评

暂无评论

Hybrid Imitation-Learning Motion Planner for Urban Driving

Hybrid Imitation-Learning Motion Planner for Urban Driving

引用

International Conference on Intelligent Transportation

作者： Cristian Gariboldi Matteo Corno Beng Jin Computer Science and Technology Xi'an Jiaotong University Xi'an China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy Full Professor with the Dipartimento di Elettronica Informazione e Bioingegneria Milano Italy Head of Autonomous Driving Department in Pix Moving Guiyang China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

With the release of open source datasets such as nuPlan and Argoverse, the research around learning-based planners has spread a lot in the last years. Existing systems have shown excellent capabilities in imitating the human driver behaviour, but they struggle to guarantee safe closed-loop driving. Conversely, optimization-based planners offer greater security in short-term planning scenarios. To confront this challenge, in this paper we propose a novel hybrid motion planner that integrates both learning-based and optimization-based techniques. Initially, a multilayer perceptron (MLP) generates a human-like trajectory, which is then refined by an optimization-based component. This component not only mini-mizes tracking errors but also computes a trajectory that is both kinematically feasible and collision-free with obstacles and road boundaries. Our model effectively balances safety and human-likeness, mitigating the trade-off inherent in these objectives. We validate our approach through simulation experiments and further demonstrate its efficacy by deploying it in real-world self-driving vehicles.

关键词： Training Visualization Tracking Roads Neural networks Trajectory Safety Planning Security Testing

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：