检索结果-内蒙古大学图书馆

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Quraishi, Suhail Javed Singh, Malikhan Prasad, Sujit Kumar Arora, Kavita Pathak, Sonal Singh, Anupam School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India BSA College of Engineering & Technology Mathura India Amity University Noida India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

The classification of underwater objects into rocks or mines is a vital task in naval security, marine exploration, and environmental studies. The current work introduces a machine learning-based approach to distinguish between rocks and mines in sonar systems utilizing logistic regression. The novelty of this approach lies in its combination of feature extraction, selection, and a logistic regression model to create an efficient and interpretable binary classifier. The data consists of acoustic signals reflected off underwater objects, which are then processed to identify relevant features. The logistic regression model is trained on a sizeable dataset made up of both hypothetical and actual examples, enabling an objective assessment of performance. The model was refined through a number of trials, which led to an improvement in the model's ability to distinguish between rocks and mines. A comparison of the suggested method with other machine learning methods highlights its effectiveness and simplicity. The study's conclusions have important ramifications for enhancing real-time decision-making in maritime navigation and naval defense. The suggested approach not only improves the precision of separating rocks from mines, but it also paves the way for more investigation into machine learning-based intelligent sonar systems. © 2023 IEEE.

关键词： Logistic Regression Machine Learning Naval Security Rock and Mine Classification Sonar Systems

来源：评论

学校读者我要写书评

暂无评论

An Overview of Semantic Based Document Summarization in Different Languages 1

An Overview of Semantic Based Document Summarization in Diff...

引用

1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Ganesh, Amal Jaya, A. Sunitha, C. Department of Computer Science and Engineering B.S.A.R Crescent Institute of Science and Technology Chennai India Department of Computer Applications B.S.A.R Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering Vidya Academy of Science and Technology Kerala Thrissur India

ISBN: (纸本)9781607685395

The evolution of Artificial Intelligence (AI) gave insight into how humans should interact with machines. Natural Language Processing (NLP) is an area under AI which aims to minimize the communication gap between human and machine. Text summarization is a sub-domain of NLP which primarily focuses on summarizing text documents into a meaningful summary. Text summarization is useful in the areas such as media monitoring, search engine optimization (SEO), question answering systems, etc. Lots of research works are carried out in the area of text summarization. Majority of the research works are adopted syntactic approaches. However, the semantic based approaches provide more meaningful summary compared to syntactic approaches. Due to the complexity of the semantic based methods, only few works are reported especially in Indian languages. In this paper, an overview of various semantic based text summarization methods are explored. An extensive review is done in languages such as English, Arabic, Chinese, Hindi, Kannada, Tamil, and Malayalam. At the end of this survey, a critical analysis was done on various semantic based text summarization techniques. This analysis will not only give insights to potential prospective researchers but also come up with an improved framework or method for semantic based abstractive text summarization. © The Electrochemical Society

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Support Vector Machine Performance with Advanced Kernel Methods 7

Improving Support Vector Machine Performance with Advanced K...

引用

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

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel function - which specifies the feature space where the data is linearly separable - has a significant impact on the performance of the model. In this research, we look at how to improve SVM performance by creating and using new kernel approaches. We contrast the effectiveness of conventional kernels like the linear, polynomial, and radial basis function (RBF) kernels with new kernel functions designed to capture complicated data structures. Extensive studies on benchmark datasets show that classification accuracy, convergence speed, and generalisability are significantly improved. applications in image recognition, bioinformatics, and financial forecasting are well-suited to the suggested kernels because to their exceptional performance in dealing with high-dimensional and non-linearly separable data. Research and practitioners in the area may benefit greatly from this study's thorough examination of the computational complexity and actual implementation of these sophisticated kernel approaches. It offers useful insights for optimising SVM performance. © 2024 IEEE.

关键词： Support vector regression

来源：评论

学校读者我要写书评

暂无评论

SIAT: Pedestrian trajectory prediction via social interaction-aware transformer

引用

Complex & Intelligent Systems 2025年第8期11卷 1-14页

作者： Wang, Chengdong Wang, Jianming Gao, Wenbo Guo, Lei School of Computer and Software Engineering Anhui Institute of Information Technology Wuhu China Yunnan Key Laboratory of Computer Technology Applications Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. The future pedestrian trajectory is predicted using a Transformer decoder that integrates both pedestrian embeddings and social graph features. Extensive experiments on the ETH/UCY and Stanford Drone datasets demonstrate that SIAT significantly outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in densely populated environments. SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction.

关键词： Pedestrian trajectory prediction Social Interaction Transformer GCN

来源：评论

学校读者我要写书评

暂无评论

Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis

Character Classification and Actor Recognition in Yakshagana...

引用

IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Anantha Murthy Prathwini Sanjeev Kulkarni Savitha G Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala and and Research Scholar Institute of Computer Science and Information Science” Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India Department of Data Science and Computer Applications Manipal Academy of higher Education Manipal Institute of Technology Manipal

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form.

关键词： YOLO Accuracy Image recognition Recurrent neural networks Machine learning Documentation Logic gates Character recognition Cultural differences Image classification

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Machine Learning Models: SVM, Naïve Bayes, Random Forest, and LSTM in Predictive Analytics 3

A Comparative Analysis of Machine Learning Models: SVM, Naï...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Singh, Malikhan Arora, Kavita Ather, Danish School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India BSA College of Engineering & Technology Mathura India Amity University in Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Machine learning models have recently developed into a crucial predictive analytics tool, driving progress in a variety of industries. Support Vector Machine (SVM), Naive Bayes, Random Forest, and Long Short-Term Memory are four well-known machine learning algorithms that are thoroughly examined and compared in this work (LSTM). By assessing these models on many parameters, including accuracy, precision, recall, and computing efficiency, the study conducts an in-depth investigation. To ensure the validity and relevance of the findings, a broad dataset is used. The comparison analysis highlights the distinct advantages and disadvantages of each model, providing information about how well suited they are for various applications. The analysis explores efficiency and interpretability as well, offering helpful direction for scholars and practitioners. The work also advances the field of predictive analytics by providing actual data on the effectiveness of these algorithms, paving the path for more in-depth and efficient application of machine learning models across a range of fields. © 2023 IEEE.

关键词： Long Short-Term Memory Machine Learning Naive Bayes Random Forest Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Navigating the Unseen: Proposing an IoT-Based Smart Shoe with Obstacle Detection for the Visually Impaired 3

Navigating the Unseen: Proposing an IoT-Based Smart Shoe wit...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Singh, Malikhan Ather, Danish School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India BSA College of Engineering & Technology Mathura India Amity University in Tashkent Uzbekistan

ISBN: (纸本)9798350342338

Navigating daily life remains a substantial challenge for the visually impaired, despite technological advancements. This paper presents an IoT-based Smart Shoe equipped with ultrasonic sensors, accelerometers, and gyroscopes to detect obstacles, providing real-time feedback to enhance navigation. The system communicates with a mobile application to offer auditory or haptic alerts, focusing on reliability, comfort, and affordability. Tested across varied terrains and lighting conditions, the prototype showed strong performance and accuracy. Visually impaired participants in trials reported increased mobility and independence. The paper also explores potential integration with navigation tools such as GPS and ethical considerations like user privacy. 'Navigating the Unseen' showcases the potential of technology in creating a more inclusive future for the visually impaired, emphasizing human centered design. © 2023 IEEE.

关键词： Assistive Technology Human-Centered Design IoT-Based Smart Shoe Obstacle Detection Visually Impaired Navigation

来源：评论

学校读者我要写书评

暂无评论

Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on Whale Optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Secure Data Embedding in Digital Images: Enhancing Covert Communication with LSB-Based Techniques

引用

Procedia computer science 2025年 258卷 2091-2100页

作者： Tina Babu Rekha R Nair Rajesh T M Kishore S Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India Department of Computer Applications Nitte (Deemed to be University) Nitte Institute of Professional Education Mangalore Karnataka India

Information security and the ability to convey secret messages have been necessities since the dawn of civilization. Various media forms, such as photos, audio files, and videos, offer a conducive environment for concealing information that is invisible to the human eye in the current digital era by taking advantage of superfluous or unused data bits. This hidden information can be used for secret communication or as a way to include more messages into a single file, which will minimize the quantity of data that needs to be sent. In order to encode and store messages within the RGB pixels of a cover image (canvas image) without causing visual distortion, two methods are shown in this study. Both approaches split the message into separate segments, which are then translated into 8-bit binary values. These values are then encrypted using a straightforward symmetric exclusive OR (XOR) encryption key, and the message is encoded in the canvas image by changing the pixel value’s least significant bit (LSB). The first approach encodes the secret message from top to bottom and left to right by sequentially storing it beginning with the top-left pixel. By encoding the message bits into the cover image pixels at random, the second technique reduces the susceptibility of the concealment to analysis. By using redundant data in digital media, these approaches can hide information while preserving the original visual integrity. This makes them an effective and safe way to communicate covertly or embed data into a single file, which maximizes data transmission.

关键词： Steganography Least Significant Bit (LSB) Data Encryption Covert Communication Digital Media XOR Encryption

来源：评论

学校读者我要写书评

暂无评论

Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network 7

Deployment Schemes of In-Band Management Mode Based on Indus...

引用

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

ISBN: (纸本)9798350350067

In order to properly manage and monitor their networks in industrial environments, industrial Software-Defined Networking (SDN) networks need to have an in-band administration mode. This research investigates a wide variety of alternative deployment options for in-band management mode in industrial SDN networks in order to increase the performance of the network, as well as its dependability and level of network security. We study how the performance of the network differs depending on whatever coupling of in-band management and industrial SDN architecture we choose. We analyse a range of potential pairings of in-band management with industrial SDN design. We also discuss the advantages and disadvantages of each deployment technique, providing insights into the creation and management of robust Industrial SDN networks. It provides useful recommendations for increasing the efficacy and security of particular network infrastructures, which might be valuable to both corporate groups and network administrators. The adoption of SDN technology and the installation of tailored management modes are becoming more and more significant as industrial settings continue to expand. This is of the utmost significance in order to guarantee the highest possible standards of both operational excellence and safety. © 2024 IEEE.

关键词： Deployment Schemes In-band Management Industrial SDN Network Network Control Network Monitoring Network Security

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：