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Developing Adaptive Deep Learning Strategies for 5G Network Management 15

Developing Adaptive Deep Learning Strategies for 5G Network ...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Patil, Shrinivas Kumar, Viranshu Goyal, Manish Kumar Karnataka Bangalore India Department of Mechnical Engineering ARKA JAIN University Jharkhand Jamshedpur India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9798350370249

this paper affords a unique method for growing deep mastering strategies for 5G community control. It leverages state-of-the-art deep neural community architectures and superior technologies inclusive of hierarchical learning, reminiscence networks, and differentiable programming to allow the machine to operate more efficaciously in an ever-changing surrounding. the gadget includes two principal components: an offline schooling community and a web auxiliary network. Each additive is related, permitting the net network to refine the educated parameters to adapt to changing occasions. the gadget is designed to deal with some of the maximum hard issues in 5G community management, along with excessive degree choice-making, resource optimization, and exceptional service enforcement. Experiments and simulations are used to evaluate the performance enhancements finished via applying the proposed technique. the consequences display that the proposed method should improve the general overall performance of 5G networks drastically. the proposed method may be used in a selection of network management packages, which include network virtualization, visitor engineering, and self-organizing networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Utilizing VGG19 and ResNet50 Neural Network Models for Comprehensive Intelligent Learning and Analysis of Legal Precedents and Texts 4

Utilizing VGG19 and ResNet50 Neural Network Models for Compr...

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4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Mathur, Anubhav Singh, Jagendra Sudheer, P. Kancharla, Rakesh Al-Farouni, Mohammed H. Priyanka School of Media Bennett University Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Hyderabad India Koneru Lakshmaiah Education Foundation Department of Cse Vaddeswaram India The Islamic University Najaf Iraq University Institute of Computing Chandigarh University Gharuan India

ISBN: (纸本)9798350387490

Analysis of legal text presents its unique challenges compared to textual data due to diverse and largely unstructured content and the sophistication of legal language. In the present study, we investigate the suitability of deep learning for this challenging domain by predicting legal precedent based on helical text and image data with the help of several state-of-the-art deep learning models such as Convolutional Neural Network (CNN), VGG16, VGG19, and ResNet50. the dataset consists of a wide range of cases across various domains: civil, criminal, administrative, constitutional, and appellate domain for a total of 350 cases. the dataset is divided into 70% for training and 30% for testing. Prior to training the models, data preprocessing includes textual cleaning, tokenization, and then converting text to a numeric format using the Bag-of-Words model. Image data is resized, normalized, and augmented to increase size by 10%. the performance of the models is assessed by the standard evaluation metric including accuracy, precision, recall, and f1 score. Results indicate that the CNN model is the best performer with 96.78% accuracy followed by VGG16 with 94.5% accuracy, VGG19 with 93.2% accuracy, and ResNet50 with 90.25% accuracy. In the discussion section, we provide insights into the performance on the strengths and weaknesses of CNN along with other models. their analysis clearly shows CNN as the most suitable model for small samples and outperforming the other models for precision, recall and F1 score. Potential use cases of this research can involve legal practice as it could greatly help lawyers carrying out precedent analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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CNN-based Deep Learning Approach for MRI-based Brain Tumor Detection 4

CNN-based Deep Learning Approach for MRI-based Brain Tumor D...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Narayanasamy, Kanagaraj Elangovan Sathish Kumar, L. Maragatharajan, M. Deepa, D. Karpagam Academy of Higher Education Department of Computer Science TamilNadu Coimbatore India Srm Institute of Science and Technology Department of Dsbs TamilNadu Kattankulathur India School of Computing Science and Engineering Vit Bhopal University MP Kotrikalan India Kongu Engineering College Department of Cse Tamil Nadu Perundurai India

ISBN: (纸本)9798350300888

Conventional brain tumor prediction method is a time-consuming and inaccurate process that involves manually evaluating a large number of MRI (magnetic resonance imaging) scan pictures in search of signs of brain cancer. this might have an effect on the patient's medical care. It may take a long time to analyse a large volume of scan data sets comprising brain pictures. Normal brain tissue and brain cancer tissue seem quite similar under the microscope, making it difficult to distinguish between the two during segmentation. A very accurate automated method for detecting cancer is crucial. this research study has developed a novel method that uses an algorithm to analyse 2D MRI human brain scans and detect brain Tumors. Combining conventional classification algorithms with deep learning methods, the approach makes use of a convolutional neural network (CNN). the model was successfully trained using a dataset of magnetic resonance imaging (MRI) scan images, including Tumors of varied sizes, locations, and intensities. Here, a SVM classifier (Support Vector Machine) and several activation methods are also used to check the results, including Softmax, RMSProp, and sigmoid. Our research showed that a CNN could attain an accuracy of 99.87%, which is higher than the present state-of-the-art findings. the CNN -based approach is meant to aid doctors in the precise identification of brain cancers in MRI scans. this innovation might greatly accelerate the pace at which patients are helped. © 2023 IEEE.

关键词： Magnetic resonance imaging

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LEARNING DECENTRALIZED PartIALLY OBSERVABLE MEAN FIELD CONTROL FOR artIFICIAL COLLECTIVE BEHAVIOR 12

LEARNING DECENTRALIZED PARTIALLY OBSERVABLE MEAN FIELD CONTR...

引用

12th international conference on Learning Representations, ICLR 2024

作者： Cui, Kai Hauck, Sascha Fabian, Christian Koeppl, Heinz Dept. of Electrical Engineering and Information Technology Technische Universität Darmstadt Germany

Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many agents. Meanwhile, collective behavior requires resolution of the aforementioned challenges, and remains of importance to many state-of-the-art applications such as active matter physics, self-organizing systems, opinion dynamics, and biological or robotic swarms. Here, MARL via mean field control (MFC) offers a potential solution to scalability, but fails to consider decentralized and partially observable systems. In this paper, we enable decentralized behavior of agents under partial information by proposing novel models for decentralized partially observable MFC (Dec-POMFC), a broad class of problems with permutation-invariant agents allowing for reduction to tractable single-agent Markov decision processes (MDP) with single-agent RL solution. We provide rigorous theoretical results, including a dynamic programming principle, together with optimality guarantees for Dec-POMFC solutions applied to finite swarms of interest. Algorithmically, we propose Dec-POMFC-based policy gradient methods for MARL via centralized training and decentralized execution, together with policy gradient approximation guarantees. In addition, we improve upon state-of-the-art histogram-based MFC by kernel methods, which is of separate interest also for fully observable MFC. We evaluate numerically on representative collective behavior tasks such as adapted Kuramoto and Vicsek swarming models, being on par with state-of-the-art MARL. Overall, our framework takes a step towards RL-based engineering of artificial collective behavior via MFC. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Markov processes

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Jointly Model-Level and Task-Level Learning for Semi-Supervised Medical Image Segmentation with Low Sample Size 4

Jointly Model-Level and Task-Level Learning for Semi-Supervi...

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4th international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2024

作者： Xu, Bin Zhao, Zhenjie School of Computer Science Nanjing University of Information Science & Technology Nanjing210044 China

ISBN: (纸本)9781643685519

Segmenting medical images automatically plays a vital role in intelligent healthcare systems. Existing methods for medical image segmentation usually rely on deep learning approaches, which require lots of labeled samples. this makes them less practical in scenarios where labeling is expensive or time-consuming. Semi-supervised learning, which leverages unlabeled data, is therefore practical to investigate. In this paper, we further consider semi-supervised medical image segmentation under low sample size conditions. We propose to learn jointly on both the model level and the task level, and through the use of two levels of supervision, the model can learn good segmentation results with even smaller sample sizes than previous semi-supervised learning methods. On the publicly available atrial segmentation challenge dataset, our method outperforms state-of-the-art methods under low sample size conditions, which indicates the superiority of our method for low sample size medical image segmentation. © 2024 the Authors.

关键词： Self-supervised learning

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Context-aware DL-based Approach Combining GNN and Attention for CSI-based Indoor Localization 4

Context-aware DL-based Approach Combining GNN and Attention ...

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4th international conference on Computer, Big Data and artificial Intelligence, ICCBD+AI 2023

作者： Sun, Daobo Yu, Haohao Dong, Tao Meng, Xiangxu Tang, Bin Harbin Engineering University College of Computer Science and Technology Harbin150000 China Harbin Engineering University Innovation and Development Base Qingdao266000 China

ISBN: (纸本)9798350373233

In recent years, due to the limitations of GPS in indoor environments, where signals may be obstructed or reflected, significant advancements have been made in indoor localization technologies to address this gap. Among these, fingerprinting localization based on Channel State Information (CSI) has garnered widespread attention owing to its robustness and cost-effectiveness. the inherent nature of deep learning (DL), with its offline training and online positioning capabilities, aligns seamlessly with CSI fingerprinting localization. With the emergence of deep learning techniques, fingerprinting localization has undergone transformative changes. However, most of the current DL-based approaches are inspired by computer vision and natural language processing, implying that they do not fully account for the physical characteristics of CSI. In this paper, we thoroughly consider the physical properties of CSI and design a context-aware approach to embed CSI into a non-Euclidean space for graph-structured learning. Moreover, in feature learning, we separately contemplate the spatial and frequency dimensions, engaging in graph-structured feature learning from distinct physical dimensions. Results on real-world datasets demonstrate that our methodology significantly outperforms state-of-the-art DL-based and meachine learning (ML)-based approaches. © 2023 IEEE.

关键词： Cost effectiveness

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Towards Reproducible Research in Machine Learning via Blockchain 4th

Towards Reproducible Research in Machine Learning via Bloc...

引用

4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Filatovas, Ernestas Stripinis, Linas Orts, Francisco Paulavičius, Remigijus Vilnius University Institute of Data Science and Digital Technologies Akademijos 4 Vilnius08663 Lithuania

ISBN: (纸本)9783031812460

artificial Intelligence, particularly in Machine Learning and related research areas such as Operational Research, currently faces a reproducibility crisis. Researchers encounter difficulties reproducing key results due to lacking critical details, including the disconnection between publications and the associated codes, data, and parameter settings. Solutions that improve code accessibility, data provenance tracking, research transparency, auditing of obtained results, and trust can significantly accelerate algorithm and model development, validation, and transition into real-world applications. Blockchain technology, with its features of decentralization, data immutability, cryptographic hash functions, and consensus algorithms, provides a promising avenue for developing such solutions. By leveraging the distributed ledger working over a peer-to-peer network, a secure and auditable infrastructure can be established for sharing and controlling data in a trusted manner. Our analysis examines the current state-of-the-art blockchain-based proposals that target reproducibility issues in the Machine Learning domain. Based on the analysis of existing solutions, we propose a high-level architecture and main modules for developing a blockchain-based platform that enhances reproducible research in Machine Learning and can be adapted to other artificial Intelligence domains. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Proportional Feature Pyramid Network for Lane Detection 4

Proportional Feature Pyramid Network for Lane Detection

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4th international conference on Computer, Big Data and artificial Intelligence, ICCBD+AI 2023

作者： Hui, Jiapeng Lian, Guoyun Wu, Jiansheng Ge, Shuting Yang, Jinfeng Shenzhen Polytechnic University Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China University of Science and Technology School of Computer Science and Software Engineering Anshan China

ISBN: (纸本)9798350373233

Lane detection under extreme conditions presents a highly challenging task that requires capturing each crucial pixel to predict the complex topology of lane lines and differentiate the various lane types. Existing methods predominantly rely on deep feature extraction networks with substantial parameters or the fusion of multiple prediction modules, resulting in large model sizes, embedding difficulties, and slow detection speeds. this paper proposes a Proportional Feature Pyramid Network(P-FPN) through fusing the weights into the FPN for lane detection. In our method, the high-level features are explored to predict lanes coarsely while local- detailed features are leveraged to improve localization accuracy. Extensive experiments on the widely used lane detection dataset, CULane dataset, demonstrate that the proposed method achieves the competitive results compared with several state-of-the-art approaches. © 2023 IEEE.

关键词： Feature extraction

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programming in an fMRI Scanner: A Report from the Field 22

Programming in an fMRI Scanner: A Report from the Field

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6th international conference on the art, science, and engineering of programming (programming)

作者： Tanimoto, Steven thompson, Rob Richards, Todd Yates, Cheri Berninger, Virginia Univ Washington Paul Allen Sch Comp Sci & Eng Seattle WA 98195 USA Colorado Sch Mines Dept Comp Sci Golden CO USA Univ Washington Dept Radiol Seattle WA USA Seattle Childrens Hosp Seattle WA USA Univ Washington Dept Educ Psych Seattle WA USA

ISBN: (纸本)9781450396561

A preliminary study is reported in which six human subjects were scanned while performing a simple form of computer programming. Functional magnetic resonance imaging typically precludes the use of ordinary computing equipment due to safety concerns when items with metallic parts are in the scanning room. We used a special visual programming environment, "Kokopelli's World," for which an optics-only trackball was sufficient. this research methodology is described. Aspects of the experience of programming, by one of the authors, in the fMRI scanner are reported. An analysis of the data from the scanning is presented, and brain areas where activity for coding exceeded activity for a control task were identified and are presented here. these observations suggest hypotheses and directions for future research. One hypothesis is that particular aspects of programming are inherently and measurably pleasurable.

关键词： programming fMRI coding in scanner visual language trackball brain imaging brain region Kokopelli's World programming experience scientific poem

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An Optimized Lightweight Crop Pest Detection Method 4

An Optimized Lightweight Crop Pest Detection Method

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4th international conference on Big Data, artificial Intelligence and Internet of things engineering, ICBAIE 2023

作者： Cheng, Zekai Huang, Rongqing Qian, Rong Zhang, Shu Jiang, Xinzhuo Liu, Meifang Qu, Xiwen Anhui University of Technology School of Computer Science and Technology Ma'anshan243032 China Institute of Agricultural Economy and Information Anhui Academy of Agricultural Sciences Hefei230031 China

ISBN: (纸本)9798350343618

the practical application of crop pest detection methods has been limited by the large number of parameters and computations, and we built a lightweight crop pest detection method YOLOLite-CSG in our previous research, which basically removed this limitation. However, further analysis shows that YOLOLite-CSG still has problems that affect the performance in terms of the prior box generation method, downsampling method and bounding box regression loss function. In response to these problems, this paper proposes the prior box generation method based on label box oversampling and clustering (BS-Medians), the downsampling method based on parallel feature transformation (PSPD-Conv), and the bounding box regression loss function with size difference optimization capability (FIoU Loss). We optimize YOLOLite-CSG using the above methods to build YOLOLite-X, an optimized lightweight crop pest detection method. the experiment results show that YOLOLite-X exceeds YOLOLite-CSG in crop pest detection precision and is higher than other state-of-the-art methods. Meanwhile, YOLOLite- X has a smaller number of parameters and computations, which is more conducive to practical deployment and application. © 2023 IEEE.

关键词： Crops

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