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共 16682 条记录，以下是141-150 订阅

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Smart Market Insights: A Deep Learning Approach to Trend Analysis 13

Smart Market Insights: A Deep Learning Approach to Trend Ana...

引用

13th International conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Ahmad, Shahnawaz Ansari, Mohd. Aquib Mair, Aakriti Hussain, Shahadat School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350380583

Stock prices are associated with the dynamic nature of the market;hence it is excruciating to predict the numbers. It has always been tough to invest in a set of assets, the oddity of the financial market does not allow traditional models to predict tomorrow's asset values with higher accuracy. Moreover, traditional models lack in finding interdependency among different sectors, overfitting of historical data, and other nonlinear factors. In this review, we examine machine learning (ML) techniques that are applied to stock market prediction. Making computers accomplish activities that ordinarily need human intellect is known as ML, and it is now the most popular trend in scientific study. According to our results, we have concluded that our Long-Short Term Memory model (LSTM) model has predicted prices with higher accuracy as compared to simpler models. the focus of this article is to develop a model for predicting future stock market values using recurrent neural networks (RNN), specifically the LSTM. Moreover, it would also highlight the key features of deep learning and why ML models are preferred over linear models for forecasting in the markets. © 2024 IEEE.

关键词： Prediction models

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Dual-Stage Deep Learning Framework for Effective Public Physical Violence Detection 13

Dual-Stage Deep Learning Framework for Effective Public Phys...

引用

13th IEEE International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Divya, Adusumilli Lakshmi, D. Sree Niveditha, Paramatmuni Lakshmi Sri, Pabolu Siva Naganjana Sreya Rohith, Venigalla Tati, Venkat Bhavan Pvpsit Computer Science Department India

ISBN: (纸本)9798350305463

Violent acts endanger both personal safety and social peace. Various solutions have been considered to minimise such behaviours, including the establishment of surveillance systems. the development of surveillance equipment that can detect aggressive behaviour autonomously is critical. the suggested system requires two stages to be executed. It must first detect the presence of persons inside video frames, followed by the extraction of frames containing potential violence. Unnecessary frames are then removed, leaving only pertinent instances of aggressive behaviour that are preserved as separate pictures. Whenever feasible, the technology recognises the faces of persons taking part in the activities.. Two pre-trained models are combined for higher accuracy and faster processing, addressing limitations of relying solely on Convolutional Neural networks. this innovative system holds promise for enhancing safety and security. this two-staged approach comprises Faster RCNN Inception V2 COCO and MobileNet V2 pre-trained models that offers improved accuracy and serves as a foundation for constructing the full model. © 2024 IEEE.

关键词： Deep learning

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Recognition of Valid QR Codes with Machine Learning 13

Recognition of Valid QR Codes with Machine Learning

引用

13th IEEE International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Minocha, Arshia Goyal, Aman Gandhi, Rashmi Amity School of Engineering and Technology Amity University Auup Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350305463

the widespread use of Quick Response (QR) codes has made QR codes an attractive target for cyberattacks, posing a security and privacy concern. Quick Response (QR) codes have revolutionized marketing strategies by providing a fast and efficient way to connect offline and online platforms. However, the security issues arise as the usage of QR codes. this paper introduces an automatic detector that identifies QR code images and lets the user know whether it is malicious or benign. To achieve this, a dataset of 10,000 original and malicious QR code images (both) is taken. Convolutional Neural Network (CNN) is employed to classify original and malicious codes. Additionally, histogram density analysis is incorporated to enhance feature identification, leading to improved classification accuracy. this paper implemented ResNet50, MobilenetV3 and InceptionV3 CNN models for evaluating the abilities to predict the malicious codes. the InceptionV3 model emerges as the most promising, attaining an accuracy of 98.13%, offering an efficient and accurate solution for document forensics and counterfeit detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Rendering 3D Quadratic Bézier Curves by Accumulation 13

Rendering 3D Quadratic Bézier Curves by Accumulation

引用

13th International conference on computer Technologies and Development, TechDev 2024

作者： Wang, Yufan Independent Researcher

ISBN: (纸本)9798331539771

this paper first proposes a method for 3D curve rendering by modeling the appearance of a parametric curve as a uniform strip of point light emitters, whilst modeling each pixel on the observing screen as a receiver that receives light in the ray direction, resulting in evaluation of a double line integral. this outlines a general framework for 3D curve rendering that is easily portable to other applications. In terms of quadratic Bézier curves, we finally derive an analytic result from our method by choosing a suitable model for a point light, which does not involve any approximation or any numerical integration analysis. Based on this, we propose a novel rendering routine for 3D quadratic Bézier curves and provide a corresponding implementation using the wgpu API. Our approach offers solutions to ensure order-independence of curve segments and continuous light intensity values on the observing screen, while avoiding common artifacts. the implementation results demonstrate the feasibility and effectiveness of our method, highlighting its potential for high-quality 3D rendering. Additionally, we identify areas for future work to further refine and optimize the rendering process. © 2024 IEEE.

关键词： Rendering (computer graphics)

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A New Market-Based Framework for Increasing Responsive Loads in Distribution Systems 13

A New Market-Based Framework for Increasing Responsive Loads...

引用

13th Smart Grid conference, SGC 2023

作者： Panahazari, Mohammad Mohebbifar, Minoo Farsani, Vahid Nazari Haghifam, Mahmoud-Reza Tarbiat Modares University Department of Electrical and Computer Engineering Tehran Iran Tabriz University Department of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798350396065

Regarding the pervasive application of information and telecommunication technologies in the power distribution industry, responsive loads (RLs) have been widely employed in the operation of distribution and transmission systems. the utilization of these loads in the competitive environment of the power market has led to a decrease in costs and an increase in the flexibility of the distribution system and, consequently, the power system. this paper presents a framework for the competitive presence of RLs in local markets. the technical cooperation method of the Distribution System Operator (DSO) and Transmission System Operator (TSO), the persuasion mechanism of DSOs, and financial signals for getting and increasing the participation of consumers are represented based on local markets and market clearing mechanisms. © 2023 IEEE.

关键词： Electric power transmission

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Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention 13

Enhancing Lighter Neural Network Performance with Layer-wise...

引用

13th International conference on computer and Knowledge Engineering, ICCKE 2023

作者： Zaravashan, Siavash Torabi, Sajjad Zaravashan, Hesam Iran University of Science and Technology Dept. Computer Engineering Tehran Iran Data Scientist Part Research Center of Ai Tehran Iran

ISBN: (纸本)9798350330151

Deep neural networks have made a revolution in different areas of Artificial Intelligence such as image classifi-cation and object detection. However, most of these models are heavy and time-consuming at the inference stage. If we desire to use real-time applications, it's necessary to lighten them up. However, this could negatively affect the performance of models. One suggested approach for enhancing the capabilities of smaller models involves a technique called knowledge distillation. In this method, a less advanced model, referred to as the student model, learns from and emulates the more advanced teacher model to enhance its performance. this method has been proven as an effective technique to compress the model and increase its accuracy. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. In this paper, we investigate the effect of connections between different layers of teacher and student networks and reveal their great importance. We have employed three different loss functions - attention-driven distillation, hierarchical context loss, and non-local loss - to enhance the classifier's overall performance. Extensive experiments on the CIFAR-100 dataset show that the proposed method has a great performance improvement. For the benefit of the research community, we will make the code for this study available on Github. © 2023 IEEE.

关键词： Image classification

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Non-Invasive Breast Cancer Screening: Leveraging InceptionV3 and GoogLeNet for thermal Image Analysis 13

Non-Invasive Breast Cancer Screening: Leveraging InceptionV3...

引用

13th International conference on computer Technologies and Development, TechDev 2024

作者： Jalloul, Reem Chethan, H.K. Alkhatib, Ramez Maharaja Institute of Technology Mysore Department of Computer Science & Engineering Karnataka India Leibniz Lung Center Forschungszentrum Borstel Biomaterial Bank Nord Borstel Germany

ISBN: (纸本)9798331539771

Breast cancer remains a leading cause of mortality worldwide, highlighting the need for early detection. this research demonstrates the potential of combining thermal imaging and deep learning for non-invasive, cost-effective breast cancer screening. this study applies deep learning models, specifically InceptionV3 for feature extraction and classifiers such as GoogLeNet, DenseNet121, and MobileNetV2, to analyze thermal images for breast cancer diagnosis. Using the Database for Mastology Research (DMR), thermal images were preprocessed and augmented to improve the dataset. Transfer learning with pre-trained models enhanced diagnostic accuracy. the InceptionV3 + GoogLeNet model achieved the highest performance with an accuracy of 96.49%, an AUC of 99.72%, a precision of 98.25%, and an F1-score of 96.55%, outperforming other combinations. Despite the promising results, the study has limitations, including a relatively small dataset and the need for further validation. Nevertheless, it paves the way for improved early detection strategies, potentially reducing breast cancer mortality rates globally. © 2024 IEEE.

关键词： Lung cancer

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FM-GAN: GAN-based Fusion of Multiple networks for Low-Light Image Enhancement 13

FM-GAN: GAN-based Fusion of Multiple Networks for Low-Light ...

引用

13th International conference of Information and Communication Technology, ICTech 2024

作者： You, KaiMao Xu, Sen Yu, Bo College of Computer Science and Technology Liaoning Provincial Key Laboratory of Industrial Intelligent Technology for Chemical Processes Shenyang University of Chemical Technology Shenyang China College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350376258

—Dark light image enhancement methods have important application value in various fields, including those based on traditional image processing techniques and those based on deep learning techniques. the current research hotspot is to use deep learning to solve this problem. However, existing enhancement methods still have the following problems: (1) Currently, the ultimate goal of most low light image enhancement methods is to enhance images to match human visual senses, without paying attention to providing convenience for object detection work. (2) Most existing enhancement methods do not pay sufficient attention to the detailed features of the image, focusing on the whole while ignoring the details, resulting in blurring and artifacts at the edges of the enhanced image details, unclear image contrast, and affecting subsequent object detection work. In response to the above issues, this article proposes an FM-GAN model suitable for low light image enhancement tasks, which includes an image generation module, an edge enhancement module, and a contrast enhancement module. the image generation module learns image features from unpaired datasets and generates high-quality normal lighting images. the edge enhancement module uses the extracted edge feature map combined with the generated image to enhance edge details and enhance the detailed features of the image. the contrast enhancement module uses dilated convolution to enhance image contrast representation, improving image quality and making image targets clearer and more visible. ©2024 IEEE.

关键词： Generative adversarial networks

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Unveiling the Nexus: Exploring the Societal Implications of AI-Driven Cybersecurity and Data Analysis 13

Unveiling the Nexus: Exploring the Societal Implications of ...

引用

13th International conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Arif, Mohd Ahmad, Shahnawaz Nazim, Mohd Ahmad, Javed Onyarin, Ogala Justin Galgotias University Department of Computer Science Engineering and Technology Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science Greater Noida India University of Delta Pmb Cyber Security Department Delta State Agbor2090 Nigeria

ISBN: (纸本)9798350380583

AI when integrated with cybersecurity and data analysis forms a new field that will have great impact on the society. the present research focuses on the social consequences of integrating AI for cybersecurity and big data analytics, thus filling the gap in the existing literature. Using both qualitative and quantitative points of view, quantitative analysis and qualitative examination, the research offers an all-embracing understanding of this convergence. It aims to review and analyze the history, present usage, future prognosis of AI related cybersecurity and data analysis, including the reflection of ethical issues, privacy aspects and relevant laws. the lessons learnt are discussed based on case studies, best practices analysis, and the study provides recommendations for the effective use of AI-based solutions throughout their life cycle. the analysis broadens the knowledge on the effects of AI technology for cybersecurity and data analytics, and helps researchers, policymakers, and industry stakeholders apply the new knowledge. © 2024 IEEE.

关键词： Cybersecurity

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MarsDeepNet: A Hybrid Deep Learning Approach for Mars Terrain Classification using Modified GoogLeNet Architecture 13

MarsDeepNet: A Hybrid Deep Learning Approach for Mars Terrai...

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13th International conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Tiwari, Raj Gaurang Bordoloi, Dibyahash Agarwal, Ambuj Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350380583

this study introduces MarsDeepNet, a hybrid deep-learning method for Martian landscape categorization. Based on GoogLeNet, the last five layers are updated with fifteen additional layers to improve feature extraction. To solve deep networks' vanishing gradient problem, we added a leaky ReLU activation function to the feature map to make the model more expressive. MarsDeepNet performed well on a publically accessible dataset, producing an accuracy of 97.94%, precision of 97.57%, recall of 96.80%, and F1-score of 94.21%. the model outperforms ResNet50, Darknet53, ShuffleNet, GoogLeNet, SqueezeNet, ResNet101, and simple CNN architectures in categorizing Martian terrains. Results show that MarsDeepNet outperforms earlier classification models and provides a solid foundation for planetary exploration research. A dependable tool for alien landscape research, this work advances remote sensing and planetary science by using modern deep-learning algorithms. Following Mars, this work may be used to study other planets and understand their geology. this study represents an advanced step towards the integration of the application of deep learning methods for the categorisation of planetary landscapes and provides a basis for the development of more advanced models/approaches. © 2024 IEEE.

关键词： Deep learning

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