检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jyotsna Talreja Wassan Haiying Wang Huiru Zheng School of Computing Ulster University Belfast United Kingdom Department of Computer Science Maitreyi College University of Delhi INDIA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Classifying microbial genes into their functional repertoire is an important task for metagenomic studies, where the research community is trying to develop Machine Learning (ML) based methods to achieve good classification performance. Random Forest (RF) has been proposed as one of the most favorable methods for such supervised analysis when applied over the abundance profiles of microbial genes mapping them to functional phenotypes. To further explore and make optimization in the existing RF model (based on the biological relationships between microbial features), a new classification method based on RF as guided by the evolutionary ancestry of microbial phylogeny, i.e. Phylogeny-RF, has been developed in this paper. This method facilitates to capture the effects of phylogenetic relatedness in a ML classifier itself. Closely related microbes by phylogeny are highly correlated and tend to have similar genetic and phenotypic traits. Such microbes behave similarly; and hence tend to be selected together or one of these could be dropped from the analysis, to make the ML process better. The proposed Phylogeny-RF algorithm has been compared with state-of-the-art classification methods including RF and the phylogeny-aware method of MetaPhyl, using 2 real-world 16S rRNA metagenomic data sets. It is observed that the proposed method performed better than the other phylogeny-driven benchmarks. For example, Phylogeny-RF attained a high AUC of 0.949 over soil microbiomes in comparison to other benchmarks.

关键词： Microorganisms Forestry Benchmark testing Soil Genetics Phylogeny Task analysis

来源：评论

学校读者我要写书评

暂无评论

Enhanced Investment Decision Making with a Reinforcement Learning-Based Multi-Agent Portfolio Management System

Enhanced Investment Decision Making with a Reinforcement Lea...

引用

Data science and Network Security (ICDSNS), International Conference on

作者： Kottooru Saaketh Raja Ram Selvaganapathy M Nandhini I Tamilselvi M Kalaivani M Natarajan B Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Chennai India Department of Electroncis and Communication Engineering C. K. College of Engineering and Technology Cuddalore India Department of Electron cis and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Department of Electron cis and Communication Engineering K. Ramakrishnan College of Engineering Trichy India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamilnadu India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The portfolio management system using traditional machine learning approaches struggles with data quality and interpretability of models, while needing to adapt to constantly evolving market dynamics. The Reinforcement Learning (RL) algorithms have proven to be a promising approach in handling diverse datasets with unpredictable performance. The proposed approach utilizes multiple RL alogrithms such as Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO), and Advantage Actor-Critic (A2C) to improve investment decision-making, historical data from the Yahoo Finance Dataset. This system incorporates three well-established reinforcement learning algorithms to dynamically adjust portfolio allocations. The A2C algorithm combines actor-critic techniques to optimize long-term portfolio performance, while the PPO algorithm maintains a balance between exploration and exploitation, enhancing returns stability. The proposed DDPG algorithm utilizes a deep neural network to determine the optimal action-value function, enabling informed investment decisions. Tested across various assets in the Yahoo Finance Dataset, it consistently outperforms traditional portfolio management methods. This flexible system can be tailored to suit different investment objectives and risk tolerances.

关键词： Heuristic algorithms Decision making Finance Reinforcement learning Network security Stability analysis Resource management Portfolios Optimization Investment

来源：评论

学校读者我要写书评

暂无评论

Efficient Tactile Sensing-based Learning from Limited Real-world Demonstrations for Dual-arm Fine Pinch-Grasp Skills

Efficient Tactile Sensing-based Learning from Limited Real-w...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiaofeng Mao Yucheng Xu Ruoshi Wen Mohammadreza Kasaei Wanming Yu Efi Psomopoulou Nathan F. Lepora Zhibin Li School of Informatics University of Edinburgh UK Touchlab Limited Edinburgh U.K. Oxford Robotics Institute University of Oxford UK Department of Engineering Mathematics University of Bristol UK Department of Computer Science University College London UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-efficient learning from demonstration framework which exploits the use of rich tactile sensing data and achieves fine bimanual pinch grasping. Specifically, we employ a convolutional autoencoder network that can effectively extract and encode high-dimensional tactile information. Further, we develop a framework that achieves efficient multi-sensor fusion for imitation learning, allowing the robot to learn contact-aware sensorimotor skills from demonstrations. The ablation studies on encoded tactile features highlighted the effectiveness of incorporating rich contact information, which enabled dexterous bimanual grasping with active contact searching. Extensive experiments demonstrated the robustness of the fine pinch grasp policy directly learned from few-shot demonstration, including grasping of the same object with different initial poses, generalizing to ten unseen new objects, robust and firm grasping against external pushes, as well as contact-aware and reactive re-grasping in case of dropping objects under very large perturbations. Furthermore, the saliency map analysis method is used to describe weight distribution across various modalities during pinch grasping, confirming the effectiveness of our framework at leveraging multimodal information. The video is available online at: https://***/BlzxGgiKfck.

关键词： Imitation learning Perturbation methods Autoencoders Grasping Robot sensing systems Feature extraction Robustness Sensors Data mining Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

arXiv

引用

arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Oblivious Markov Decision Processes: Planning and Policy Execution

Oblivious Markov Decision Processes: Planning and Policy Exe...

引用

IEEE Conference on Decision and Control

作者： Murtadha Alsayegh Jose Fuentes Leonardo Bobadilla Dylan A. Shell School of Computing and Information Sciences Florida International University Miami FL USA Department of Computer Science & Engineering Texas A&M University College Station TX USA

We examine a novel setting in which two parties have partial knowledge of the elements that make up a Markov Decision Process (MDP) and must cooperate to compute and execute an optimal policy for the problem constructed from those elements. This situation arises when one party wants to give a robot some task, but does not wish to divulge those details to a second party-while the second party possesses sensitive data about the robot's dynamics (information needed for planning). Both parties want the robot to perform the task successfully, but neither is willing to disclose any more information than is absolutely necessary. We utilize techniques from secure multi-party computation, combining primitives and algorithms to construct protocols that can compute an optimal policy while ensuring that the policy remains opaque by being split across both parties. To execute a split policy, we also give a protocol that enables the robot to determine what actions to trigger, while the second party guards against attempts to probe for information inconsistent with the policy's prescribed execution. In order to improve scalability, we find that basis functions and constraint sampling methods are useful in forming effective approximate MDPs. We report simulation results examining performance and precision, and assess the scaling properties of our Python implementation. We also describe a hardware proof-of-feasibility implementation using inexpensive physical robots, which, being a small-scale instance, can be solved directly.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pre-trained Convolutional Neural Networks and Otsu’s Multi-level Thresholding based Alzheimer’s Classification

Pre-trained Convolutional Neural Networks and Otsu’s Multi-...

引用

Advances in computing, Communication and Applied informatics (ACCAI), International Conference on

作者： Nivedhitha Mahendran P M Durai Raj Vincent Duraimurugan Samiayya Venkatesan Rajinikanth Department of Networking and Communications School of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India School of Computer Science and Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Technology St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Division of Research and Innovation SIMATS Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Disorders affecting the brain and the nerves that supply the entire body are referred to as neurological diseases. Changes in the brain’s chemistry, structure, or electrical system can cause a variety of symptoms. Alzheimer’s disease (AD), a degenerative neurological condition that impairs cognitive function, is one example of this type of illness. The most prevalent type of dementia for which there is no effective treatment. Early detection and treatment, however, may help the patient’s lifestyle and reduce symptoms. Brain imaging techniques, including MRI, CT, PET, and others, are widely used to diagnose neurological problems. It has been discovered that the Artificial Intelligence based approaches, such as the deep learning algorithms and machine learning algorithms work well for assessing these photos and provide improved accuracy for identifying AD. In this paper, we classified AD and Control cases using four different Convolutional Neural Networks (CNN) versions. Additionally, in order to increase the accuracy of AD’s classification, we have segmented the images using Otsu’s thresholding technique. The outcomes demonstrate that, both before and after thresholding was implemented, the CNN AlexNet offers a significant improvement over the other deep learning models.

关键词： Neurological diseases Deep learning Thresholding (Imaging) Machine learning algorithms Magnetic resonance imaging Computed tomography Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Smart Agriculture: A Comprehensive and Analytical Survey

Smart Agriculture: A Comprehensive and Analytical Survey

引用

International Conference on Contemporary computing and informatics (IC3I)

作者： Zatin Gupta Amit Bindal Isha Chopra Maharishi Markandeshwar Engineering College MM (DU) Ambala Haryana Department of Computer Science School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Department of CSE Maharishi Markandeshwar Engineering College MM(DU) Ambala Haryana India

The development of various technologies has been beneficial, to varying degrees, for the many different kinds of businesses that exist today. Several other industries, including the agricultural sector, have benefited from implementing technology based on the Internet of Things (IoT) in crop production. Consequently, the following review paper has attempted to present the influence that the Internet of Things technology has had on the development of intelligent agriculture. This paper will review intelligent agricultural systems using the Internet of Things. These systems were developed in recent years. To arrive at these conclusions, the research paper has discussed several aspects of intelligent agriculture and the benefits of technology based on the Internet of Things. In addition, a review paper was presented in which the Internet of Things (IoT) technology was broken down into its parts in great detail. Several particulars hamper the application, including the high cost, energy consumption, and the current knowledge gap. In addition, the solutions to the problems are simultaneously and coherently; the use of secondary qualitative methods has made it simpler to discuss the requirements of smart agriculture. The information presented in the paper reflects concrete knowledge regarding the application of IoT systems in intelligent agriculture.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Timestamps as Prompts for Geography-Aware Location Recommendation

arXiv

引用

arXiv 2023年

作者： Luo, Yan Duan, Haoyi Liu, Ye Chung, Fu-Lai Department of Computing The Hong Kong Polytechnic University Hong Kong Media Lab Massachusetts Institute of Technology United States College of Computer Science and Technology Zhejiang University China

Location recommendation plays a vital role in improving users’ travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp. © 2023, CC BY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Evaluation of biometric communication and authenticate recognition using ANN with PSO algorithm

引用

Concurrent Engineering Research and Applications 2022年

作者： Umasankari, N. Muthukumar, B. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Department of Information Technology DMI College of Engineering Chennai India

This research investigates the novel techniques which provide the detailed information on the biometric images used along with the methods applied for biometric image pre-processing. It also describes the proposed methodology which was implemented with the method of optimized Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) algorithm for classification of attributes. In the current work, a big effort has been implemented for designing an efficient technique for recognizing the biometric images, especially for the modalities like finger print and retina image. Initially, the pre-processing module used the method of histogram equalization to enhance the contrasts of entire image in order to get the best image quality. This makes the image adaptable for further processing. Next, the feature extraction module has the involvement of two image sets (finger print and retina image). The Gray Level Co-occurrence Matrix (GLCM) was used for extracting the needed features in this module. Next is Feature Based Fusion Technique (FBFT) for reducing the features for authentication purpose. This research work uses the FBFT to get fused feature vector. Finally, deals with the non-recognition and recognition of the images. The images were tested by using Artificial Neural Network (ANN). Here, the recognition is done by ANN and the optimization is done by the sophisticated function of Particle Swarm Optimization Algorithm (PSOA). ANN does the classification of images as recognized and non-recognized and yields best results. © The Author(s) 2022.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Driven Cryptanalysis in Modern Encryption Systems

Deep Learning-Driven Cryptanalysis in Modern Encryption Syst...

引用

2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Radhakrishnan, Venkateswaran Rodda, Vijayakumari Sowmiya, S. Sarker, Abdur Rahman Palanikumar, R. Asokan, R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Dhofar Oman Krishna University Department of Computer Science Rudravaram Machilipatnam521004 India Sri Eshwar College of Engineering Coimbatore641202 India Applied Statistics and Data Science Jahangirnagar University Dhaka Bangladesh Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India Kongunadu College of Engineering and Technology Department of Ece Tamil Nadu Trichy India

ISBN: (纸本)9798331521394

With the rapid advancements in artificial intelligence, deep-learning-based cryptanalysis has emerged as a promising approach to evaluating the security of modern encryption algorithms. Traditional cryptanalysis methods, such as linear and differential attacks, often require extensive mathematical modelling and computational effort. In contrast, deep learning enables automated feature extraction and pattern recognition, potentially identifying cryptographic weaknesses more efficiently. This paper presents an advanced neural network-based cryptanalysis framework targeting modern encryption schemes, including AES, RSA, and lightweight block ciphers. We employ deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformer models to analyse ciphertext-plaintext relationships and recover cryptographic keys. Additionally, we explore the impact of data preprocessing, hyperparameter optimization, and model interpretability on cryptanalysis accuracy. Our experiments demonstrate that deep learning can achieve significant improvements in key recovery probability compared to conventional approaches. However, we also highlight the computational challenges and practical limitations of deep-learning-based cryptanalysis. This study provides insights into the applicability of AI-driven techniques in cryptanalysis and their implications for cryptographic security. © 2025 IEEE.

关键词： Convolutional neural networks

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：