检索结果-内蒙古大学图书馆

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Supreeth, A. Jeeva Ganesh, A. Subash, N. Santhosh, R. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Accurate and efficient land-use classification is important for the formulation of plans by urban planners, environmental monitoring, and even sustainable development. The paper explores the use of transfer learning methods that utilize pre-trained models to improve LSTM and DNNs' performance when applied on metropolitan settings for LAC. This can be done using transfer learning to learn from large datasets and adapt to the complexities of urban land-use patterns even with limited labeled data. This study uses the UC-Merced-LandUse dataset and heatmap analysis to visualize the models' classification decisions in demonstrating the effectiveness of this approach. This research contributes to the advancement of land-use classification techniques, which enable more accurate and informed urban planning and environmental management. © 2025 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Preventive Environment for Pest Over the Crops Using Simplified Network Architecture 4

A Preventive Environment for Pest Over the Crops Using Simpl...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kuhanraja, A.R. Ramkumar, P. Balaji, S. Santhosh, R. Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Crop pests can significantly impact productivity, making early detection and prevention crucial for management. Machine learning approaches are being used to forecast crop diseases using a variety of data sets. Relatively easy access to the environment data has resulted in a lot of attempts at predicting diseases and pests using this data. In this research, we provide a model that uses deep learning technique known as Simplified YOLO 3.1 architecture to examine past crop growth environment data to forecast illnesses. Utilizing large public datasets on crops such as tomato, pepper, grape, strawberry, and paprika, we show that the model is capable of accurately forecasting crop pest and disease risk. With an average accuracy of 98% and precision of 96%, it showed excellent predictive power. The anticipated results can help with post-harvest processing or insect control. It is expected that this learning framework and crop disease prediction model, which is based on environmental data, will be extensively useful for the prevention of pests and diseases in various facilities and crops. © 2025 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

来源：评论

学校读者我要写书评

暂无评论

Sign Comm: A Real-Time Indian Sign Language Recognition System Using Deep Learning for inclusive communication 2

Sign Comm: A Real-Time Indian Sign Language Recognition Syst...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ilakkia, A. Kanchanadevi, P. Abirami, A. Rajan, R. Muthukumaran, K. Artificial Intelligence and Data Science Sri Manakula Vinayagar Engineering Puducherry India St. Joseph College of Arts and Science Department of Computer Science Cuddalore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Individuals who are deaf often utilize sign language for communication. In Sign Language, the symbols or motions are arranged grammatically. Every motion is referred to as a sign. Indian Sign Language Recognition using Deep Learning model describes the creation of SignComm, which improves communication for those with hearing loss by translating Indian Sign Language (ISL) motions into text. To identify and interpret ISL gestures in real-time. The paper describes the difficulties the deaf community faces due to the dearth of real-time sign language interpretation services. It suggests a method that uses an Automatic Sign Detection system with deep learning models. The research describes current systems and their shortcomings, such as the lack of real-time translation tools and the difficulties the deaf community faces in obtaining sign language interpretation. Next, it presents the suggested system, SignComm, which has benefits including quick sign language decoding, a user-friendly platform that is easy to use and can foster an inclusive atmosphere in public areas and educational institutions a literature review that examines several deep learning models and their applicability to the project is included in the paper. It emphasizes the potential of the ResNet-50 architecture and the significance of a unique ISL dataset. The benefits of the suggested method, including its accuracy, inclusivity, and capacity to address communication barriers, are highlighted in the paper. Additionally, it outlines the steps involved in the implementation process, such as gathering data, training the ResNet model, and deploying in real time. A user-friendly experience for converting ISL gestures into text is the main goal of the intricate user interface design. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Automation of IoT Network Traffic Analysis and Anomaly Detection using Deep Packet Inspection with Hybrid DenseNet121-BiLSTM 1

Automation of IoT Network Traffic Analysis and Anomaly Detec...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Data Science Coimbatore India Department of Mathematics Thrissur India Department of Mathematics Mala India

ISBN: (纸本)9798331529246

Network security is one of the most crucial problems facing in Internet of Things. An essential component of information systems' long-term viability and regular functioning is network intrusion detection. One potential technique that aids in the identification and defense against network threats is deep packet inspection (DPI). A novel DPI method utilizing a blend of LSTM, GRU, and Recurrent Neural Networks is presented in the paper. The proposed model enhances the accuracy and reliability of detecting outcomes for the threats included in network traffic data. The proposed system detects and identify particular threats, such as malware exploitation, DoS attacks, and unauthorized access attempts, allowing linked devices and services with an accuracy of 99.99%. The system, therefore, can be applied in real-time monitoring systems, intrusion detection and prevention systems, and forensic investigations. Through a series of quantitative trials and comparisons with the most recent formulations, the effectiveness of this approach is assessed. © 2025 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Scheduling with Obligatory Tests 32

Scheduling with Obligatory Tests

引用

32nd Annual European Symposium on Algorithms, ESA 2024

作者： Dogeas, Konstantinos Erlebach, Thomas Liang, Ya-Chun Department of Computer Science Durham University United Kingdom Data Science Institute Columbia University New YorkNY United States Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9783959773386

Motivated by settings such as medical treatments or aircraft maintenance, we consider a scheduling problem with jobs that consist of two operations, a test and a processing part. The time required to execute the test is known in advance while the time required to execute the processing part becomes known only upon completion of the test. We use competitive analysis to study algorithms for minimizing the sum of completion times for n given jobs on a single machine. As our main result, we prove using a novel analysis technique that the natural 1-SORT algorithm has competitive ratio at most 1.861. For the special case of uniform test times, we show that a simple threshold-based algorithm has competitive ratio at most 1.585. We also prove a lower bound that shows that no deterministic algorithm can be better than √2-competitive even in the case of uniform test times. © Konstantinos Dogeas, Thomas Erlebach, and Ya-Chun Liang;licensed under Creative Commons License CC-BY 4.0.

关键词： Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

data-Efficient Radiology Report Generation via Similar Report Features Enhancement 3rd

Data-Efficient Radiology Report Generation via Similar Repo...

引用

3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Li, Yanfeng Sun, Jinghan Wang, Liansheng Department of Computer Science at School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (纸本)9783031820069

The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiology

来源：评论

学校读者我要写书评

暂无评论

Unveiling Early Autism Spectrum Disorder Detection: A Comprehensive Study of Machine Learning and Deep Learning Approaches 6

Unveiling Early Autism Spectrum Disorder Detection: A Compre...

引用

6th IEEE Pune Section International Conference, PuneCon 2023

作者： Tarai, Trilochan Parhi, Manoranjan Mishra, Debahuti Shaw, Kailash University Department of Computer Science and Engineering Odisha Bhubaneswar India University Centre of Data Science Department of Computer Science and Engineering Odisha Bhubaneswar India Department of Aiml Pune India

ISBN: (纸本)9798350324204

According to research by the world health organization (WHO), approximately 0.63% of children are diagnosed with autism spectrum disorder (ASD). ASD commonly emerges during childhood and persists through adolescence and adulthood. Early detection of ASD is crucial as it can lead to effective interventions and improved outcomes. Machine learning and deep learning techniques have gained significant recognition for their efficacy in various fields including biomedicine and medicine. These models offer valuable tools for data interpretation in clinical diagnostics and decision-making processes. Consequently, researchers emphasize the utility of both machine learning and deep learning methods for disease screening. This study aims to leverage both machine learning and deep learning techniques to classify ASD cases using a dataset sourced from the UCI repository. The dataset encompasses pediatric cases. The classification task employed k-nearest neighbors' algorithm (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Among these, CNN outperformed SVM and k-NN, achieving a remarkable accuracy of 99.71%. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning

See Detail Say Clear: Towards Brain CT Report Generation via...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zheng, Chengxin Ji, Junzhong Shi, Yanzhao Zhang, Xiaodan Qu, Liangqiong College of Computer Science Beijing University of Technology Beijing China Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798891761681

Brain CT report generation is significant to aid physicians in diagnosing cranial diseases. Recent studies concentrate on handling the consistency between visual and textual pathological features to improve the coherence of report. However, there exist some challenges: 1) Redundant visual representing: Massive irrelevant areas in 3D scans distract models from representing salient visual contexts. 2) Shifted semantic representing: Limited medical corpus causes difficulties for models to transfer the learned textual representations to generative layers. This study introduces a Pathological Clue-driven Representation Learning (PCRL) model to build cross-modal representations based on pathological clues and naturally adapt them for accurate report generation. Specifically, we construct pathological clues from perspectives of segmented regions, pathological entities, and report themes, to fully grasp visual pathological patterns and learn cross-modal feature representations. To adapt the representations for the text generation task, we bridge the gap between representation learning and report generation by using a unified large language model (LLM) with task-tailored instructions. These crafted instructions enable the LLM to be flexibly fine-tuned across tasks and smoothly transfer the semantic representation for report generation. Experiments demonstrate that our method outperforms previous methods and achieves SoTA performance. Our code is available at https://***/ChaunceyJheng/PCRL-MRG. © 2024 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

引用

computers, Materials & Continua 2022年第4期71卷 717-734页

作者： Siti Hajar Khairuddin Mohd Hilmi Hasan Emilia Akashah P.Akhir Manzoor Ahmed Hashmani Centre for Research in Data Science Department of Computer and Information SciencesUniversiti Teknologi PETRONASPerakMalaysia

Fuzzy inference system(FIS)is a process of fuzzy logic reasoning to produce the output based on fuzzified *** system starts with identifying input from data,applying the fuzziness to input using membership functions(MF),generating fuzzy rules for the fuzzy sets and obtaining the *** are several types of input MFs which can be introduced in FIS,commonly chosen based on the type of real data,sensitivity of certain rule implied and computational *** paper focuses on the construction of interval type 2(IT2)trapezoidal shape MF from fuzzy C Means(FCM)that is used for fuzzification process of mamdani *** the process,upper MF(UMF)and lower MF(LMF)of the MF need to be identified to get the range of the footprint of uncertainty(FOU).This paper proposes Genetic tuning process,which is a part of genetic algorithm(GA),to adjust parameters in order to improve the behavior of existing system,especially to enhance the accuracy of the system *** novel process is a hybrid approach which produces Genetic Fuzzy System(GFS)that helps to enhance fuzzy classification problems and *** approach provides a new method for the construction and tuning process of the IT2 MF,based on the FCM *** result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA *** is shown that the proposed approach is able to outperform the mentioned benchmarked *** work implies a wider range of IT2 MF types,constructed based on FCM outcomes,and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction,analytics and rule-based solutions.

关键词： Fuzzy inference system membership function genetic tuning lateral adjustment trapezoidal MF fuzzy C means

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：