检索结果-内蒙古大学图书馆

14th International Conference on Software Technology and engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Ganga, Krishna INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

CNN-DBO: Dung Beetle Optimization Based Urban Area Change Detection Using Satellite Images

CNN-DBO: Dung Beetle Optimization Based Urban Area Change De...

引用

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

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Improving Cybersecurity Measures Through the Revelation of Malignant URLs using Machine Learning Techniques 5

Improving Cybersecurity Measures Through the Revelation of M...

引用

5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Saravanan, T.M. Muthusamy, A. Thamil Selvi, C.P. Senthil Kumar, M. Maheshwari, D. Kongu Engineering College Department of Computer Technology Perundurai Erode India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thudupathi Erode India Kpr College of Arts Science and Research Department of Computer Technology Coimbatore India

ISBN: (纸本)9798350389609

In today's world the internet has ingrained itself deeply into our lives, and web searching has become essential for people of all ages, locations, and occupations. However, due to the rise in internet usage, there has been a rise in spoofing attacks through malicious websites. Online purchases, reservations, recharges, and various other transactions are now commonly conducted online. Internet users are increasing;it has become crucial to develop automatic URL detection systems to protect users. This proposed research aims to compare different categorization models like Decision Tree, Random Forest and Logistic Regression to determine which one achieves finest accuracy in distinguishing among genuine websites and False websites. Objective is to identify attacking sites effectively and determine the artificial intelligence algorithm that provides the best accuracy for this use. © 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

Adaptive Learning in Multiagent Systems Using Brain-computer Interfaces 2

Adaptive Learning in Multiagent Systems Using Brain-Computer...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dhatterwal, Jagjit Singh Kaswan, Kuldeep Singh Thapliyal, Nitin Malik, Kiran Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Galgotias University Department of Computer Science and Engineering UP Greater Noida India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Matu Ram Institute of Engineering and Management Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350371055

Adaptive learning in multiagent systems has emerged as a promising approach to enhance agents' capabilities to adapt to dynamic environments and optimize their performance. In this research paper, we investigate the integration of brain-computer interfaces (BCIs) as a novel means to facilitate adaptive learning in multiagent systems. BCIs establish direct communication channels between the human brain and external devices, enabling real-time monitoring and interpretation of neural activity. By leveraging BCIs, agents can capture valuable cognitive signals from human operators and use this information to adapt their decision-making processes and behaviors. We present a theoretical framework that outlines the incorporation of BCIs into multiagent systems and the mechanisms for adaptive learning. Additionally, we propose a comprehensive methodology for evaluating the effectiveness of this approach. The results of our experiments demonstrate the potential of adaptive learningin multiagent systems using BCIs, paving the way for new applications in various domains, including human-machine col-laboration, assistive technologies, and interactive gaming systems. © 2024 IEEE.

关键词： Adaptive Learning Brain-computer Interfaces Human-Agent Interaction Human-Machine Collaboration Multiagent Systems

来源：评论

学校读者我要写书评

暂无评论

Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Wild-Animal Detection near Roads using IoT Sensor and Optimization

Machine Learning based Wild-Animal Detection near Roads usin...

引用

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Simonthomas, S. Rohith, K. Shalini, K.Shantha Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Intelligence and Data Science Chennai India

ISBN: (纸本)9798331539948

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system's precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic Algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain. © 2024 IEEE.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

Hate Speech Detection Using Enhanced Recurrent Neural Network Based on Elephant Herding Optimization on Social Media 3

Hate Speech Detection Using Enhanced Recurrent Neural Networ...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Senthilkumar, K. Sathiyabama, S. Ayyamuthukumar, D. Thiruvalluvar Government Arts College Department of Computer Science Namakkal Rasipuram India Department of Artificial Intelligence and Data Science Muthayammal College of Engineering Rasipuram India

ISBN: (纸本)9798350369083

In the past ten years, there has been a rise in nasty behaviors on social media due to the increased use of these platforms. One of the most offensive of these behaviors is hate speech, so users must safeguard themselves against it on social media. This research presents an approach to detect hate speech from social media using an optimized Elman recurrent neural network (ERNN) based on the hybrid local search algorithm (LSA) and elephant herding optimization (EHO) algorithm called LSAEHO. The LSA-EHO algorithm is used to LSA to obtain the optimal solution of EHO, enhance the convergence rate, and avoid the local optima problem of EHO. The LSA-EHO is used to find appropriate hyperparameters for ERNN to enhance the convergence rate and accuracy. The experimental results confirmed that the developed hate speech prediction-based optimized ERNN produced higher prediction accuracy and a faster convergence rate. © 2024 IEEE.

关键词： Elman neural networks

来源：评论

学校读者我要写书评

暂无评论

Application of Machine Learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bharathi, S.V.Shri Sai, Paidipati Lakshman Ayyappa, Paidipati Charan Ram Reddy, Kakuru Jaya Praveen Reddy, Gangireddy Sai Preetham Rajesh, Katuri Madhava SRMIST- Ktr Department Of Data Science And Business Systems Tamil Nadu Chennai India Vit Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350352689

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices. © 2024 IEEE.

关键词： Water quality

来源：评论

学校读者我要写书评

暂无评论

SAND: semi-automated adaptive network defense via programmable rule generation and deployment

引用

science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

来源：评论

学校读者我要写书评

暂无评论

A Corpus-Based Auto-encoder-and-Decoder Machine Translation Using Deep Neural Network for Translation from English to Telugu Language

引用

SN computer science 2023年第4期4卷 354页

作者： Mahanty, Mohan Vamsi, Bandi Madhavi, Dasari Department of Computer Science and Engineering Vignan’s Institute of Information Technology (A) Visakhapatnam 530046 India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology and Science Madanapalle 517326 India Department of Computer Science and Engineering Sridevi Women’s Engineering College Hyderabad 500075 India

There is a huge demand for machine translation to design automated auto-encoders that can convert English to the Telugu language. Neural machine translation (NMT) is a new and effective technology that has significantly benefited traditional machine translation strategies. As a result, the primary goal of this work is to propose an auto-encoder–decoder-based deep neural network for translating English to the Telugu language. Deep neural networks are used for learning, where input language is supplied to the neural network following data processing and analysis. The neural network then does auto-tuning, which helps to improve the translation quality. This proposed work produces target sentences in the Telugu language, resulting in a higher Bilingual Evaluation Understudy (BLEU) Score and a lower Word Error Rate (WER). The BLEU finds the similarity of the translated text by Machine Translation models with high-quality references and WER finds better accuracy in identifying the target sentence. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep neural network Encoder-and-decoder English-to-Telugu Neural machine translation

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：