检索结果-内蒙古大学图书馆

IEEE International conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olakunle L. Ojo Yinka Ajiboye Adeniran S. Afolalu Ademola T. Morebise Ifeoluwa M. Omoyajowo Oladipo E. Abe Olugbenga Olumodimu David S. Adeyeye Olugbenga M. Agboola Oyelami Olawunmi Department of Mathematical and Physical Sciences Afe Babalola University Ado Ekiti Nigeria Department of Mathematical & Physical Sciences Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Mechanical Engineering Science University of Johannesburg South Africa Department of Mathematical and Physical Sciences Afe Babalola University Ado-Ekiti Ekiti State Nigeria Students Affair Unit Afe Babalola University Ado Ekiti Nigeria Department of Physics Federal University Oye Ekiti Institute of Cosmology and Gravitation University of Portsmouth UK Department of Geological Sciences Achievers University Owo Ondo State Nigeria Department of Tourism and Events Management Afe Babalola University Ado Ekiti Nigeria Geography Department University of Lagos Lagos Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Satellite image processing is a cornerstone of modern Earth observation, providing critical data for meteorology, resource management, and urban studies. The integration of artificial intelligence (AI) has significantly enhanced image analysis through machine learning, deep learning, and data fusion techniques, improving efficiency and accuracy. This study examines AI-driven transformations in satellite image processing, focusing on applications in disaster monitoring, urban growth assessment, hyperspectral vegetation analysis, and air quality evaluation. We also explore AI algorithms such as supervised and unsupervised learning models, convolutional neural networks (CNNs), and data fusion techniques, which facilitate complex image interpretation. Additionally, challenges in AI integration with satellite systems are discussed, along with future research directions to advance Earth observation and environmental monitoring. Overall, this review highlights AI’s indispensable role in satellite imagery for global decision-making.

关键词： Earth Deep learning Ethics Machine learning algorithms Accuracy Data integration Vegetation mapping Satellite images Environmental monitoring Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Optimization and Application of a Deep Learning-Based Multi-Class Retinal Vessel Segmentation Model

Optimization and Application of a Deep Learning-Based Multi-...

引用

Computing, Robotics and System Sciences (ICRSS), International conference on

作者： Xingyu Di Artificial Intelligence and Integrated Circuit Engineering College of Communications Nanjing Institute of Technology Nanjing China

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

With the development of deep learning, its applications in computer vision and medical image processing have become increasingly widespread, particularly excelling in tasks like image segmentation. Convolutional neural networks (CNNs), known for their ability to perform multi-level feature extraction and their adaptable structural design, have become pivotal in the field of medical image analysis. Retinal vessel multi-class segmentation, which is crucial for the early diagnosis of ophthalmic diseases and the extraction of pathological features, has increasingly become a central focus of research. This paper addresses the field of multi-class segmentation of retinal vessels and proposes an optimized deep learning-based model architecture, significantly improving segmentation accuracy and robustness. The model innovatively integrates the feature extraction capabilities of CNN sand incorporates techniques such as the Adam optimizer, dynamic learning rate adjustment, regularization constraints, and early stopping to enhance convergence efficiency and generalization performance. Compared to traditional single-class segmentation methods, this model effectively distinguishes arteries, veins, and their overlapping regions, demonstrating strong adaptability in complex vessel networks and ambiguous areas. Through the application of data augmentation methods, including rotation, flipping, and scaling, the preprocessing process boosts sample diversity, which in turn enhances the model's robustness against varied data. Experimental results reveal that the optimized model delivers outstanding performance across various segmentation tasks and achieves critical breakthroughs in multi-class vessel recognition. While there remains room for improvement in handling intricate details and complex scenarios, the model's multi-class segmentation capabilities and medical application value offer significant insights for future research.

关键词： image segmentation Analytical models Adaptation models image analysis Veins Feature extraction Retinal vessels Robustness Optimization Diseases

来源：评论

学校读者我要写书评

暂无评论

Inside the Spectrogram: Convolutional neural networks in Audio processing

Inside the Spectrogram: Convolutional Neural Networks in Aud...

引用

International conference on Sampling Theory and applications

作者： Monika Dorfler Roswitha Bammer Thomas Grill NuHAG Faculty of Mathematics Austrian Research Institute for Artificial Intelligence

ISBN: (纸本)9781538615669

Convolutional neural networks have established a new standard in many machine learning applications not only in image but also in audio processing. In this contribution we investigate the interplay between the primary representation mapping a raw audio signal to some kind of image (feature) and the convolutional layers of an ensuing neural network. We introduce a new notion of equivalence of feature-network pairs and show the relation of feature and networks for the example of mel-spectrogram input on the one hand and varying analysis windows on the other hand.

关键词： neural network audio signals image feature Audio Spectrogram equivalence imagery (Psychotherapy) images Hand

来源：评论

学校读者我要写书评

暂无评论

Leveraging LSTM-RNN combined with SVM for Network Intrusion Detection

Leveraging LSTM-RNN combined with SVM for Network Intrusion ...

引用

Proceedings of the International conference on Data Science, Machine Learning and artificial Intelligence

作者： Shweta Patidar Meenakshi Tripathi Subham Kumar Gupta

ISBN: (纸本)9781450387637

Abstract---Computer networks and their applications have changed the daily aspects of human life considerably be it business, entertainment, work or travelling aspects. However, this dependency on computer networks has also made the networks prone to various attacks. Therefore, it becomes very important to secure the network from any type of suspicious activity. In place of securing each system individually, we can provide security solutions to network traffic data. The concepts of Deep Learning and Machine Learning have been effectively used in the fields of image processing, pattern recognition, NLP, etc. Considering the advantages of these concepts and the sequential nature of traffic data, we have proposed a solution for detecting intrusions in the computer network traffic using one of the Deep Learning approach called Long Short-Term Memory(LSTM). The proposed model makes use of LSTM combined with the loss function of SVM to detect intrusion in Kyoto University Honeypot Network Traffic Data. With an accuracy of 97.10%, true positive rate of a 98.50% and a false positive rate of 5.21%, the proposed LSTM model outperforms other existing models in detecting intrusions over Kyoto University Dataset.

关键词： deep learning intrusion detection system(IDS) long short-term memory (LSTM) machine learning recurrent neural network(RNN) squared hinge loss support vector machine(SVM)

来源：评论

学校读者我要写书评

暂无评论

A machine learning-based approach for mercury detection in marine waters

A machine learning-based approach for mercury detection in m...

引用

IEEE International conference on Data Mining Workshops (ICDM Workshops)

作者： Francesco Piccialli Fabio Giampaolo Vincenzo Schiano di Cola Federico Gatta Diletta Chiaro Edoardo Prezioso Stefano Izzo Salvatore Cuomo Department of Mathematics and Applications “R. Caccioppoli” University of Naples Federico II Napoli Italy

Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.

关键词： image recognition Pipelines neural networks Metals Feature extraction Prediction algorithms Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Biometric Recognition of African Clawed Frogs 20th

Biometric Recognition of African Clawed Frogs

引用

20th International conference on Computer Analysis of images and Patterns (CAIP)

作者： Prins, Fabian L. Tomanin, Dario Kamenz, Julia Azzopardi, George Univ Groningen Bernoulli Inst Math Comp Sci & Artificial Intelli Groningen Netherlands Univ Groningen Groningen Biomol Sci & Biotechnol Inst Mol Syst Biol Groningen Netherlands

ISBN: (纸本)9783031442391;9783031442407

The African clawed frog (Xenopus laevis) is a commonly used model organism for cell biological, developmental, and biomedical research. For health monitoring and experimental quality control purposes, it is desirable to identify individual frogs regularly throughout their life. Current methods for identification are often invasive and associated with significant investment costs. Identification based on images of the biometric pattern on a frog's back has been implemented in some laboratories, but so far has been performed manually and therefore is time-consuming and limited to small group sizes. This work proposes a novel pipeline for data acquisition, pre-processing, and training of a classification model based on pattern recognition. The pipeline is structured around laboratory frog colonies and smartphone usage. In order to achieve a lightweight system in our evaluation we consider a MobileNet ConvNet pre-trained on imageNet. Two feature sets are evaluated on a new data set of 1,647 image samples collected from 160 frogs: RGB images, and 3-channel contour maps (i.e. CORF3D). The results indicate that the CORF3D feature set is favoured over RGB. CORF3D achieved the best performance of 99.94% average accuracy, while RGB had the best performance of 98.79%. Analysis of misclassifications shows that bad predictions are often caused by bad lens focus, light reflections, and positional inconsistency in pattern extraction, which can be addressed during data acquisition. The proposed methodology is, therefore, an effective solution for the recognition of Xenopus laevis.

关键词： Frog recognition Biometric analysis Convolutional neural networks Contour maps CORF3D

来源：评论

学校读者我要写书评

暂无评论

image and Video Captioning Using Deep Learning and Natural Language processing

Image and Video Captioning Using Deep Learning and Natural L...

引用

International conference on Computing Communication Control and Automation (ICCUBEA)

作者： Manoj Naidu Athrva Kulkarni Sahil Kadam Siddhesh Joshi Nilesh P. Sable Anuradha Yenkikar Department of CSE – Artificial Intelligence Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Deep learning models have been a huge success in image recognition which hence can be used for the purpose of text generation. In the field of imaging science, captioning images and videos is regarded as an intellectually difficult job. Visual Geometry Group (VGG); is a standard deep Convolutional neural Network (CNN) architecture with multiple layers, specifically focusing on the integration of CNN for image feature extraction. Exploring this underlying method, the use of another model is essential for caption generation. Here the Recurrent neural Network (RNN) comes in use for caption generation from the extracted features. Models named Long Short-Term Memory (LSTM) based on RNN and Bidirectional encoder representation transformer (BERT) based on Transformers have been prominent in ensuring accurate results. The Flicker8k dataset is used which provides a variety of information useful for model training. By testing validation data along with evaluation metrics, we analyze the effectiveness of different models to create consistent and descriptive headlines. Extending our inquiry to encompass title generation using transformer models, while also exploring learning techniques for real-time title generation and delivery using the Open-CV library available in Python to get the output from the camera and display it on screen. The result shows that the LSTM is the best model for captioning, with an accuracy of 65.07% at the epochs of 300 and the BERT model has an accuracy of 31% at the epochs of 2. The findings of this study not only contribute to advancing subtitle enhancement methodologies but also broaden the potential applications of deep learning techniques in this domain.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Bidirectional control Transformers Feature extraction Encoding Data models Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Recent progress in optoelectronic memristors for neuromorphic and in-memory computation

引用

NEUROMORPHIC COMPUTING AND ENGINEERING 2023年第2期3卷 022002-022002页

作者： Pereira, Maria Elias Martins, Rodrigo Fortunato, Elvira Barquinha, Pedro Kiazadeh, Asal NOVA Univ Lisbon NOVA Sch Sci & Technol & CEMOP Dept Mat Sci UNINOVA i3N-CENIMAT P-2829516 Caparica Portugal

Neuromorphic computing has been gaining momentum for the past decades and has been appointed as the replacer of the outworn technology in conventional computing systems. artificial neural networks (ANNs) can be composed by memristor crossbars in hardware and perform in-memory computing and storage, in a power, cost and area efficient way. In optoelectronic memristors (OEMs), resistive switching (RS) can be controlled by both optical and electronic signals. Using light as synaptic weigh modulator provides a high-speed non-destructive method, not dependent on electrical wires, that solves crosstalk issues. In particular, in artificial visual systems, OEMs can act as the artificial retina and combine optical sensing and high-level image processing. Therefore, several efforts have been made by the scientific community into developing OEMs that can meet the demands of each specific application. In this review, the recent advances in inorganic OEMs are summarized and discussed. The engineering of the device structure provides the means to manipulate RS performance and, thus, a comprehensive analysis is performed regarding the already proposed memristor materials structure and their specific characteristics. Moreover, their potential applications in logic gates, ANNs and, in more detail, on artificial visual systems are also assessed, taking into account the figures of merit described so far.

关键词： optoelectronic memristors (OEMs) photonic memristors resistive switching devices artificial neural networks (ANNs) artificial visual systems neuromorphic computing

来源：评论

学校读者我要写书评

暂无评论

Loop Closure Detection Based on Improved Hybrid Deep Learning Architecture

Loop Closure Detection Based on Improved Hybrid Deep Learnin...

引用

IEEE International Symposium on Parallel and Distributed processing with applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yuan Liu Rong Xiang Qieshi Zhang Ziliang Ren Jun Cheng Guilin University of Electronic Technology Guilin China Key Laboratory of Human-Machine Intelligence-Synergy Shenzhen China

ISBN: (纸本)9781728152103

Loop closure detection plays a vital role in the visual Simultaneous Localization and Mapping (SLAM) system. Traditional Bag-of-Visual-Word (BoVW) methods for loop closure detection has been successfully applied to various SLAM systems. The Convolutional neural networks (ConvNets) based methods can automatically learn feature representation from original image, and it is more robust to illumination changes. However, these two methods all have some limitations. In order to overcome the shortcomings of the traditional methods extract insufficient features, and the ConvNets based methods unable to adapt complex environment changes, in this paper, we propose an improved hybrid deep learning architecture (HDLA) to generate high-level semantic image features for loop closure detection specifically. This network is based on hybrid ConvNet modified by us to especially cope with robust and real-time feature extraction. We present extensive experiments on three real world datasets to evaluate each of the specific challenges in loop closure detection. The results demonstrate that the proposed method achieves superior performances and provides a feasible solution for loop closure detection.

关键词： convolutional neural nets feature extraction image classification image representation learning (artificial intelligence) object detection robot vision SLAM (robots) robot vision Feature extraction image representation Object detection image classification surgical closure Loops Contraceptive Coil Feasible Solution original image simultaneous localization and mapping

来源：评论

学校读者我要写书评

暂无评论

Mobile Medical Assistant and Analytical System for Dengue Patients

Mobile Medical Assistant and Analytical System for Dengue Pa...

引用

Advancements in Computing (ICAC), 2019 International conference on

作者： K. T. K. Jayampathi M. A. C. Jananjaya E. P. C. Fernando Y. A. Liyanage M. G. N. M. Pemadasa G. W. D. A. Gunarathne Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408639

Dengue fever is a vector-borne viral disease spread by the mosquito Aedes Aegypti. It is a public health problem, with an estimated 50-500 million infections each year and no effective vaccination. People's hectic schedules may not have enough time to see a doctor every time they have a fever. They may overlook their disease, believing it to be a common ailment. Prior medical assistance for dengue patients with fever to check their conditions reliably is a major problem. There is no easily accessible proper system to identify dengue patients at an early stage. This paper presents a mobile medical assistant and analytical system for dengue patients. With a novel approach, using the most appropriate technologies, the mobile application supports identifying dengue patients using the chatbot, analyzing skin conditions, analyzing blood reports, and analyzing dengue-infected areas' functionalities. The registered users can log in to the system and check their dengue condition. The development is carried out with Natural Language processing, artificial neural Network (ANN), Machine Learning, image processing, Convolutional neural Network (CNN), and Android technologies. A mobile application prototype is created and tested, with the possibility of future testing and implementation. The results show effective performances in analyzing dengue conditions.

关键词： Schedules Skin Mobile applications Vaccines Convolutional neural networks Reliability Viruses (medical)

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：