检索结果-内蒙古大学图书馆

27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Tigani, Smail Makrane, Amal Saadane, Rachid Chehri, Abdellah AAIR Lab Digital Engineering and Artificial Intelligence Systems High Private School Casablanca20100 Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This research paper presents a deep learning-based predictive model for classifying currency trends using technical indicators. The model is trained on a dataset generated from three technical indicators: relative strength index (RSI), moving average convergence divergence (MACD), and stochastic. The dataset consists of historical currency data along with the corresponding values of the technical indicators. The deep learning model can accurately classify the trends of a given currency based on the importance of these indicators. The model's performance is evaluated using standard metrics, and the results demonstrate its effectiveness in classifying currency trends. The proposed model provides a valuable tool for traders and investors in the foreign exchange market by helping them make informed decisions about the direction of currency prices. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Algorithmic Trading Deep Learning Predictive Analytics

来源：评论

学校读者我要写书评

暂无评论

Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2023年第1期24卷 1339-1349页

作者： Anna Hedström Leander Weber Dilyara Bareeva Daniel Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne Understandable Machine Intelligence Lab TU Berlin Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Department of Computer Science University of Potsdam Potsdam Germany Department of Electrical Engineering and Computer Science TU Berlin and Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Berlin Germany Understandable Machine Intelligence Lab TU Berlin Berlin Germany and BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin Germany

The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus--a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://***/understandable-machine-intelligence-lab/Quantus/).

关键词： explainability responsible AI reproducibility open source Python

来源：评论

学校读者我要写书评

暂无评论

Pipe-BD: Pipelined Parallel Blockwise Distillation

Pipe-BD: Pipelined Parallel Blockwise Distillation

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Hongsun Jang Jaewon Jung Jaeyong Song Joonsang Yu Youngsok Kim Jinho Lee Department of Electrical and Computer Engineering Seoul National University Department of Artificial Intelligence Yonsei University Department of Computer Science Yonsei University CLOVA Image Vision CLOVA AI Lab NAVER

Training large deep neural network models is highly challenging due to their tremendous computational and mem-ory requirements. Blockwise distillation provides one promising method towards faster convergence by splitting a large model into multiple smaller models. In state-of-the-art blockwise distillation methods, training is performed block-by-block in a data-parallel manner using multiple GPUs. To produce inputs for the student blocks, the teacher model is executed from the beginning until the current block under training. However, this results in a high overhead of redundant teacher execution, low GPU utilization, and extra data loading. To address these problems, we propose Pipe-BD, a novel parallelization method for blockwise distillation. Pipe-BD aggressively utilizes pipeline parallelism for blockwise distillation, eliminating redundant teacher block execution and increasing per-device batch size for better resource utilization. We also extend to hybrid parallelism for efficient workload balancing. As a result, Pipe-BD achieves significant acceleration without modifying the mathematical formulation of blockwise distillation. We implement Pipe-BD on PyTorch, and experiments reveal that Pipe-BD is effective on multiple scenarios, models, and datasets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Secrecy Energy Efficiency Maximization in IRS-Assisted VLC MISO Networks with RSMA: A DS-PPO Approach

引用

IEEE Transactions on Wireless Communications 2025年

作者： Guo, Yangbo Fan, Jianhui Zhang, Ruichen Chang, Baofang Ng, Derrick Wing Kwan Niyato, Dusit Kim, Dong In Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Engineering Lab of Intelligence Business and Internet of Things Henan453007 China Nanyang Technological University College of Computing and Data Science Singapore Singapore University of New South Wales School of Electrical Engineering and Telecommunications NSW2052 Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem by jointly optimizing the beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices. The problem is constrained by total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, the maximum ratio transmission (MRT) and zero-forcing (ZF) are adopted to design the beamforming vectors. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches. Moreover, the implementation of the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment. © 2002-2012 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

引用

computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

来源：评论

学校读者我要写书评

暂无评论

Improving Formation in Swarm Robotics with a Leader-Follower Approach

Improving Formation in Swarm Robotics with a Leader-Follower...

引用

IEEE International Conference on Mechatronics and Automation

作者： Mazen Bahaidarah Ognjen Marjanovic Fatemeh Rekabi-Bana Farshad Arvin Department of Electrical & Electronic Engineering University of Manchester Manchester UK King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Swarm & Computational Intelligence Lab (SwaCIL) Durham University Durham UK

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Collective Motion (CM) is a bio-inspired swarm behaviour observed in nature, such as schooling fish and flocking birds. In swarm robotics, CM is attained through the implementation of virtual viscoelastic links that generate attractive and repulsive forces among robots. This study investigates the challenges associated with maintaining formation integrity in specific geometrical arrangements, such as ring configurations. To address this, a Leader-Follower (L-F) approach is proposed to enhance the swarm shape stability. Simulation results demonstrated that the proposed method significantly optimised CM performance, achieving alignment of nearly 99% of the robots within 50 s. This shows the effectiveness of the L-F approach in successfully implementing swarm formation with a significant performance improvement.

关键词： Mechatronics Shape Navigation Robot kinematics Simulation Noise Swarm robotics

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Learning Using Noisy-Latent Augmentation

Self-Supervised Learning Using Noisy-Latent Augmentation

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Rasa Khosrowshahli Shahryar Rahnamayan Azam Asilian Bidgoli Masoud Makrehchi Department of Electrical Computer and Software Engineering Nature-Inspired Computational Intelligence (NICI) Lab Ontario Tech University Oshawa ON Canada Engineering Department Nature-Inspired Computational Intelligence (NICI) Lab Brock University St. Catharines ON Canada Nature-Inspired Computational Intelligence (NICI) Lab Faculty of Science Wilfrid Laurier University Waterloo ON Canada Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada

Generally speaking, labeled data is difficult and expensive to provide for applications in machine learning and data mining. One of the earliest approaches to tackle this problem is semi-supervised self-training to take advantages of labeled and unlabeled data to create pseudo-labeled data. However, reaching a high level of confidence to predict the unseen data by a classifier with a limited number of labeled samples is a challenging task. Generating pseudo-labeled data can collaboratively improve the self-training to increase its confidence for further predictions. This paper proposes a collaborative framework between augmentation and self-training to accurately train a model with very limited labeled data. Our framework includes two components where the first component is an unsupervised technique to augment labeled data and feed to the second component which is self-training. The first component uses a custom variational autoencoder architecture (VAE) to generate new samples by adding randomly generated noise to encoded latent representation. As a result, the augmentation component can generate the unique and unexplored images with respect to limited input data distribution. We evaluated the proposed framework on handwritten MNIST image dataset. The conducted experiment shows that the generative component can be helpful in overcoming the problem of inaccurate self-training prediction when sufficient labeled data is not accessible.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Open and Fully Decentralised Platform for Safe Food Traceability

An Open and Fully Decentralised Platform for Safe Food Trace...

引用

International Conference on Computational science and Computational intelligence (CSCI)

作者： Eric Tutu Tchao Elton Modestus Gyabeng Andrews Tang Joseph Barnes Nana Benyin Eliel Keelson John Jerry Kponyo Department of Computer Engineering Kwame Nkrumah University of Science and Technology Ghana Distributed IoT Platforms Privacy and Edge-Intelligence Research Lab Department of Telecommunications Engineering Kwame Nkrumah University of Science and Technology Ghana Responsible Artificial Intelligence Lab

Concerns about food safety have grown across society in recent years. Building a trustworthy traceability system is essential for effectively identifying and preventing food safety issues as well as tracing the responsible parties. The entire food supply chain, which includes the stages of production, processing, warehousing, transportation, and sale, must be precisely recorded, shared, and traced. Traditional traceability systems suffer from problems like data invisibility, tampering, and the leakage of sensitive information. This paper proposes an open platform for a food safety traceability system that indefinitely and incessantly stores and records all transactions, events, and activities on the blockchain's immutable ledger linked with IPFS - a peer-to-peer decentralised file system - for storing and providing maximum transparency and traceability. The platform leverages the blockchain's characteristics such as immutability, transparency, smart contracts, and consensus algorithms to make it ideal for food safety traceability systems. But more importantly, it mirrors the food supply chain making it a pluggable toolbox for all stakeholders across the food chain to adapt to their system irrespective of the food products they deal with since it is a multi-asset system as well. It could be even adapted for non-food products that have a supply chain similar to the typical food supply chain. Simulation results show that there is the complete success of all the blockchain transactions on our platform with real-time responsiveness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Diverse Image Generation with Diffusion Models and Cross Class label Learning for Polyp Classification

arXiv

引用

arXiv 2025年

作者： Sharma, Vanshali Jha, Debesh Bhuyan, M.K. Das, Pradip K. Bagci, Ulas Department of Computer Science & Engineering Indian Institute of Technology Guwahati Guwahati781039 India Department of Electronics & Electrical Engineering Indian Institute of Technology Guwahati Guwahati781039 India Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago60611 United States

Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precursors to CRC, can pathologically be classified into two major types: adenomatous (malignant potential) and hyperplastic (benign). For precise classification and early diagnosis of such polyps, the medical procedure of colonoscopy has been widely adopted paired with various imaging techniques, including narrow band imaging (NBI) and white light imaging (WLI). These imaging modalities have different advantages in capturing polyp-specific features for accurate classification. However, the existing classification techniques mainly rely on a single imaging modality and show limited performance due to data scarcity. Recently, generative artificial intelligence has been gaining prominence in overcoming such issues. Additionally, various generation-controlling mechanisms using text prompts and images have been introduced to obtain visually appealing and desired outcomes in a better-controlled manner. However, such mechanisms require class labels to make the model respond efficiently to the provided control input. In the colonoscopy domain, such controlling mechanisms are rarely explored;specifically, the text prompt is a completely uninvestigated area. Moreover, the unavailability of expensive class-wise labels for diverse sets of images limits such explorations. Therefore, in this work, we develop a novel model, PathoPolyp-Diff, that generates text-controlled synthetic images with diverse characteristics in terms of pathology, imaging modalities, and quality. In the process, we introduce cross-class label learning to make the model learn features from other classes, reducing the burdensome task of data annotation. We validate the effectiveness of text-controlled synthesis and cross-class label learning by performing polyp classification (adenomatous/hyperplastic) with different imaging modalities (NBI/WLI) and text prompts. The

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

Feasibility-Guaranteed Safety-Critical Control with Applications to Heterogeneous Platoons

arXiv

引用

arXiv 2023年

作者： Liu, Shuo Xiao, Wei Belta, Calin Department of Mechanical Engineering Boston University BrooklineMA02215 United States Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology CambridgeMA United States Department of Electrical and Computer Engineering The Department of Computer Science University of Maryland College ParkMD United States

This paper studies safety and feasibility guarantees for systems with tight control bounds. It has been shown that stabilizing an affine control system while optimizing a quadratic cost and satisfying state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBF) and Control Lyapunov Functions (CLF). One of the main challenges in this method is that the QP could easily become infeasible under safety constraints of high relative degree, especially under tight control bounds. Recent work focused on deriving sufficient conditions for guaranteeing feasibility. The existing results are case-dependent. In this paper, we consider the general case. We define a feasibility constraint and propose a new type of CBF to enforce it. Our method guarantees the feasibility of the above mentioned QPs, while satisfying safety requirements. We demonstrate the proposed method on an Adaptive Cruise Control (ACC) problem for a heterogeneous platoon with tight control bounds, and compare our method to existing CBF-CLF approaches. The results show that our proposed approach can generate gradually transitioned control (without abrupt changes) with guaranteed feasibility and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

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：