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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是221-230 订阅

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Cylindrical thompson Sampling for High-Dimensional Bayesian optimization 27

Cylindrical Thompson Sampling for High-Dimensional Bayesian ...

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27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Rashidi, Bahador Johnstonbaugh, Kerrick Gao, Chao Huawei Canada Ottawa ON Canada

Many industrial and scientific applications require optimization of one or more objectives by tuning dozens or hundreds of input parameters. While Bayesian optimization has been a popular approach for the efficient optimization of blackbox functions, its performance decreases drastically as the dimensionality of the search space increases (i.e., above twenty). Recent advancements in high-dimensional Bayesian optimization (HDBO) seek to mitigate this issue through techniques such as adaptive local search with trust regions or dimensionality reduction using random embeddings. In this paper, we provide a close examination of these advancements and show that sampling strategy plays a prominent role and is key to tackling the curse-of-dimensionality. We then propose cylindrical thompson sampling (CTS), a novel strategy that can be integrated into single- and multi-objective HDBO algorithms. We demonstrate this by integrating CTS as a modular component in state-of-the-art HDBO algorithms. We verify the effectiveness of CTS on both synthetic and real-world high-dimensional problems, and show that CTS largely enhances existing HDBO methods.

关键词： optimization

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Research on Semantic SLAM Algorithm Based on Deep learning 4

Research on Semantic SLAM Algorithm Based on Deep Learning

引用

4th international conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Wang, Xiulian Shi, Zihao College of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9781510682528

In practical applications, mobile robots or UAVs often need to navigate and locate in a dynamic environment, but traditional SLAM algorithms often perform poorly in the face of dynamic environments. therefore, dynamic SLAM has become one of the research hotspots. In order to solve the problems of low positioning accuracy and poor robustness of traditional visual SLAM in dynamic scenes. In this paper, an improved algorithm based on ORBSLAM3 is proposed. Under the condition of keeping the original framework unchanged, the algorithm adds a new semantic thread, combines Mask R-CNN to segment the image frame, extracts the keyframes for optimization, removes the feature points of the dynamic object, and retains the feature points of the original static object. Finally, comparative experiments are carried out based on the TUM dataset, and the final results show that the proposed algorithm is superior to the existing algorithms, and the positioning accuracy and robustness are improved. © 2024 SPIE.

关键词： SLAM robotics

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Automated Loan Approval System using Machine learning 4

Automated Loan Approval System using Machine Learning

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Srivastava, Swati Gla University Department of Computer Engineering and Applications UP Mathura India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

In today's world, it's common for people to apply loans from banks and financial institutions for various reasons. But not everyone who applies can be approved. We often hear about cases where individuals fail to repay their loans, causing significant losses for banks. Making decisions about loan approvals is risky and challenging. Previous works considered single algorithm to make predictions where accuracy is low. To enhance the accuracy is a challenge. So, the goal of this study is to use multiple machine learning algorithms to extract key information about the loan data which could help organizations make better decisions about approving or rejecting loan requests from customers. In this paper, we analyze real bank credit data and apply various machine learning algorithms to determine the creditworthiness of customers. the accuracies of 56.28%, 59.46%, 57.34%, 63.23%, and 73.79% are obtained for logistic regression, support vector machine, Decision tree classifier, Random Forest Classifier, and ensemble method respectively. the aim of this work is to train a machine learning model to predict the eligibility for the applied loan and to create an interface for different types of loans. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-objective Biofilm Algorithm to Resolve optimization Problems 4th

Multi-objective Biofilm Algorithm to Resolve Optimization Pr...

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4th international conference on Data, Engineering, and applications, IDEA 2022

作者： Devi, R. Vasundhara Sathya, S. Siva Department of Computer Applications Perunthalaivar Kamarajar Arts College Puducherry Kalitheerthalkuppam India Department of Computer Science Pondicherry University Puducherry India

ISBN: (纸本)9789819700363

Usually, swarm intelligence (SI) algorithms are simulated based upon the behaviors of living organisms and their problem solving techniques. these algorithms are useful in solving the real-world, combinatorial optimization problems which do not demand a firm solution. Even though various swarm intelligence algorithms exist based on the behaviors of number of birds, animals, or insects, one algorithm cannot solve all optimization problems altogether. therefore, till now, a new swarm intelligence algorithm with intriguing features is always recommended. In this article, a fresh multi-objective biofilm algorithm (MOBifi) is tested with bi-objective mathematical benchmark test functions and a comparison with its counterparts is provided. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： optimization algorithms

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Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. this early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. the method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. the classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. the method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. this exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

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FODAS: A Novel Reinforcement learning Approach for Efficient Task Scheduling in Fog Computing Network 9

FODAS: A Novel Reinforcement Learning Approach for Efficient...

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9th IEEE international conference on Fog and Mobile Edge Computing (FMEC)

作者： Nagabushnam, Ganesan Choi, Yundo Kim, Kyong Hoon Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9798350366495;9798350366488

In heterogeneous fog-cloud computing networks, efficiently scheduling aperiodic tasks is an NP-hard problem, particularly when aiming to minimize makespan, adhere to deadlines, and conserve energy. this paper introduces a novel scheduling algorithm, Fog-Optimized Deadline-Adaptive Scheduling (FODAS), which combines Earliest Deadline First (EDF) principles with Deep Multi-Agent Reinforcement learning, incorporating Proximal Policy optimization (PPO) and Recurrent Neural Networks (RNN). FODAS is specifically designed to manage aperiodic tasks in heterogeneous fog-cloud environments, prioritizing deadline adherence and energy efficiency. the proposed algorithm begins by collecting tasks into a global scheduling queue, and sorting them by their deadlines. It incorporates three homogeneous schedulers within a heterogeneous framework, ensuring tasks meet their deadlines and achieve notable energy savings. Key performance metrics such as deadline meeting rate, makespan, and energy savings are evaluated, comparing FODAS against single-agent reinforcement learning algorithms such as PPO and Asynchronous Advantage Actor-Critic (A3C). Our findings reveal that FODAS significantly improves the rate of meeting deadlines by up to 18% compared to the conventional algorithms. Additionally, it delivers substantial energy savings, with improvements of up to 80% in certain setups, and markedly decreases makespan, achieving reductions of up to 57.3% compared to traditional algorithms. the proposed algorithm also demonstrates exceptional operational efficiency, reducing the time required for scheduling tasks, particularly in high-density node networks. these results underscore the effectiveness of FODAS in managing complex task scheduling within fog-cloud computing environments.

关键词： Fog computing Cloud Computing Task scheduling Multi-Agent Reinforcement learning Deadline Heterogeneous Aperiodic task Proximal Policy optimization Recurrent Neural Network

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2024 4th international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

2024 4th International Conference on Emerging Frontiers in E...

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4th IEEE international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

ISBN: (纸本)9798331528614

the proceedings contain 92 papers. the topics discussed include: proactive electric vehicle braking system;optimal unit commitment and dispatch of electric vehicles in microgrid using walrus optimization algorithm;cell voltage equalization method for battery-powered applications;resolving uncertainty with decisions for electric vehicle aggregators;modeling of lithium-ion battery with thermal analysis;predictive thermal management systems for lithium ion batteries using linear regression for enhanced efficiency and longevity;real time environmental fault detection and diagnosis in photovoltaic systems;comparative analysis of control algorithms for DSTATCOM in grid-connected wind energy system;and SPR based refractive index sensor with multilayered gold and graphene: a bio-sensing approach using wave theory based delineation.

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A hybrid approach for Capacitated Vehicle Routing Problem with Time Windows based on Conflict-Driven Clause learning and Reinforcement learning 5

A hybrid approach for Capacitated Vehicle Routing Problem wi...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Christy, A. Shyry, Prayla S. Praveena, Anto Helen, Suji L. Sathyabama Inst Sci & Technol Dept CSE Chennai Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

In the present scenario, the major applications of Vehicle Routing Problem (VRP) is food delivery. Many algorithms are available to solve the variants of problems related to VRP in minimizing the service time. In this paper, an algorithm is proposed for Vehicle Routing Problem with Time windows (VRPTW) considering traffic prediction. this algorithm is constructed based on Conflict-Driven Clause learning and Reinforcement learning(CLRL). Experiments were constructed for inter-distance route optimization and intra-distance route optimization. the proposed algorithm shows that the CLRL algorithm is more flexible and scalable in solving multi-path and multi-driver problem under VRPTW compared to Dijkstra's algorithm and Google's operations research tool.

关键词： Vehicle Routing Problem Dijkstra's algorithm Google OR tool Reinforcement learning reward, etc

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A QoS-Aware Energy optimization Framework for Mobile Devices Using DRL and Edge Computing 4

A QoS-Aware Energy Optimization Framework for Mobile Devices...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Mokgethi, Chandapiwa Sigwele, Tshiamo Selvaraj, Rajalakshmi Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

the proliferation of computationally intensive mobile applications like Augmented Reality (AR), Speech Recognition, and Mobile Gaming has led to an alarming rise in mobile energy consumption, raising the need for frequent recharging of mobile devices. this frequent recharging leads to degradation of mobile device's battery health resulting in reduced battery capacity, shorter usage cycles, and ultimately increased costs associated with battery replacements. the current mobile energy optimization frameworks are unable to effectively handle the dynamic and realtime nature of mobile environments with unpredictable variables. this research proposes a Deep Reinforcement learning (DRL) model to optimize energy efficiency in mobile devices using Mobile Edge Computing (MEC) to handle varying workloads and dynamic environment while maintaining Quality of Service (QoS) where computationally intensive applications are optimally offloaded to edge cloud servers. the result shows that the proposed outperforms current works by reducing energy consumption and average latency by 27.5% and 35% respectively. © 2024 IEEE.

关键词： Mobile edge computing

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Intelligent Water Body Delineation for Flood Monitoring using Remote Sensing Data and Deep learning algorithms 4

Intelligent Water Body Delineation for Flood Monitoring usin...

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4th international conference on Artificial Intelligence and Signal Processing

作者： Mathana, Senthil Kumari K. Chitra, P. thiagarajan, Priya Thiagarajar Coll Engn Dept Comp Applicat Madurai Tamil Nadu India Thiagarajar Coll Engn Dept Comp Sci & Business Syst Madurai Tamil Nadu India

ISBN: (纸本)9798350350661;9798350350654

Floods pose significant threats to lives, infrastructure, and the environment. Flood monitoring is crucial for effective disaster management and mitigation. Traditional methods of flood monitoring have several limitations including manual data collection, limited coverage, and delayed response times. In recent years, advanced remote sensing technology, coupled with the power of deep learning algorithms have greatly enhanced flood monitoring capabilities. this paper presents a comprehensive investigation into the integration of remote sensing data and deep learning algorithms for water body delineation in flood monitoring. A dataset containing 700 Sentinel-2 satellite images was used. It was divided into training, testing, and validation data, with corresponding ground truth masks. Deep learning models, including UNET, SEGNET, DeepLabv3, and RefineNet, were employed for water body delineation tasks. After thorough assessment, RefineNet emerged as the best performing model. It has exceptional accuracy and precision in outlining water bodies. Transfer learning techniques were explored to further improve the RefineNet model performance. Various backbone architectures, including ResNet50, ResNet101, Mobi leNetV2, and EfficientNetB0, were investigated for their compatibility with RefineNet. Experimental results revealed that EfficientNetB0 served as the optimal backbone architecture, significantly enhancing the model's performance. the RefineNet model with EfficientNetB0 backbone achieved remarkable results, with an accuracy of 0.9478, F1-score of 0.9162, IOU of 0.8455, and MAP of 0.8708. these findings underscore the potential of integrating remote sensing data and deep learning algorithms for flood monitoring applications. the findings from this research carry substantial importance for disaster management and emergency response efforts using neural networks.

关键词： Flood monitoring Remote sensing Satellite imagery Deep learning Water body delineation Sentinel-2 Transfer learning Refine-net EfficientNet Disaster management Environmental monitoring Emergency response Accuracy assessment Model evaluation Flood detection

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