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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Maximizing Privacy in Reinforcement learning with Federated Approaches

Maximizing Privacy in Reinforcement Learning with Federated ...

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Intelligent Technologies (CONIT), international conference on

作者： Shiva Mehta Sumeet Singh Sarpal Centre for Research Impact &Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Data protection issues and security in the age of technological advancement have become problematic, mainly because these methods are increasingly widespread. this paper covers critical problems of Federated Reinforcement learning (FRL) and shows how this technique could provide a viable solution to the issue. We are attempting the use case scenario of the Federation Q-learning in the Labyrinth difficulties here. It ensures that technological requirements are adapted at all levels and keeps the information secret. A trial has been made to test the application of IRL on AI training, enabling the scientists to acquire actual proof of the advantages of FRL against the theoretical approaches in training, which are the majority. the main quantitative facet of this case uses pretty scientific statistics. Research results from conventional Q-learning and our variation, codel bean Q-learning, are significant in drawing conclusions and gaining insights. the federated q-learning presented scores around 25% higher than the q-learning with an agent, which is one more advantage than the q-learning using an agent. Just as Federated Q-learning obtains fast policy optimization by reducing convergence time by 2 5%, parallelism also brings about quicker and more effective exploration of the Q-table. Federated Q-learning is no exception to the general rule that this model has an advantage in ensuring data privacy, yielding an evident four-point improvement in the data privacy score. this shows that this technology quickly becomes a trusted ally and can anonymize data without compromising users’ privacy and security. Moreover, in the final stage of our work, the implementation of the model pretty soon caused an improvement in computer efficiency by 40%. the results showed that the current traditional AIL techniques suddenly faced five times more risks concerning privacy and security.

关键词： Training Privacy Q-learning Statistical analysis Data protection Parallel processing Privacy breach

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Recommendations for a Vertical Farming System Using Hydroponics, machine learning and IoT

Recommendations for a Vertical Farming System Using Hydropon...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Basmala Mosaad Raed Abdulla Muhammad Ehsan Rana School of Engineering Asia Pacific University of Innovation and Technology (APU) Kuala Lumpur Malaysia School of Computer Computing Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur Malaysia

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

Agriculture nowadays faces several problems because of the rise of the global population, which increased the food demand and caused instability in food security. Organic food that is pesticide-free is also in high demand, leading to consuming more arable lands, water, pesticides, and fossil fuels. Vertical farming is a promising solution to these challenges. It is a method of growing plants in vertically stacked layers, which can be done indoors or outdoors. Hydroponics is a method of growing plants without soil, using a nutrient-rich water solution. Vertical farming using hydroponics can solve these problems by integrating IoT and AI technology. It reduces water wastage and eliminates pesticide consumption. the aim of this research is to develop recommendations for a Vertical Farming System using hydroponic based on machine learning and IoT technology for a better, controlled, and monitored growth of plants. the hydroponic system and its parameters are monitored and controlled by a GUI using the Blynk app that allows the user to control the food quality by its nutrients and parameters to help in producing more organic food. Crop yield health is being predicted by an AI model that detects the healthy and unhealthy leaves with an accuracy of 82%, hence saving many farmers’ time. the system is also applicable for personal use, allowing individuals or families to plant their own food.

关键词： Temperature sensors Temperature measurement Training Hydroponics machine learning Pesticides Predictive models

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A Data Commercialization Evaluation Model Based on Fuzzy Multi-attribute Group Decision Making 23

A Data Commercialization Evaluation Model Based on Fuzzy Mul...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Bin Zhao Li Cao Yilong Gao Peng Zhai School of Information Science and Engineering Linyi University China School of Mathematics and Computer Application Technology Jining University China

ISBN: (纸本)9798400716669

the core purpose of data commoditization transformation is to lower the threshold of data utilization and facilitate data analysis and mining at the user end. In this paper, we propose a data commoditization degree evaluation model for data trading websites. First, the evaluation index system of data commercialization is constructed, combining the abstract indexes reflecting data commonality and industry-specific data commercialization standards to assess the degree of data commercialization; second, the fuzzy multi-attribute group decision-making method is utilized to conduct fuzzy evaluation of the indexes in response to the issue of user evaluation preference; and the fuzzy function corresponding to the fuzzy evaluation is calculated based on the fuzzy structured element afterwards. the degree of superiority of the program is calculated by fuzzy compromise group decision-making method. Finally, the simplicity, convenience, and effectiveness of the data commoditization assessment model are demonstrated through a speech data case study.

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application of fuzzy clustering based on improved whitening weight function in vocational education evaluation 23

Application of fuzzy clustering based on improved whitening ...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Yijun Feng Wanning Zhu Lei Hong Youfeng Li Shaoyu Tang School of Software Engineering Jinling Institue of Technology China

ISBN: (纸本)9798400709517

In recent years, the importance of vocational education development has increased significantly. the key to improving vocational education lies in addressing the evaluation problem of vocational education schools. However, previous solutions to this problem suffered from issues like subjective evaluation methods and distorted data. this paper presents a vocational education evaluation method that combines gray clustering and fuzzy clustering, while also improving the form of the whitening weight function. the initial membership degree is calculated using the whitening weight function, followed by optimization using fuzzy C-means clustering. Experimental results demonstrate that this algorithm effectively combines the advantages of gray clustering and fuzzy C-means clustering, enabling more accurate clustering of vocational education schools in the evaluation process. this approach facilitates more objective evaluation and provides a quantitative basis for vocational education evaluation.

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Deep learning Methods for Precise Sugarcane Disease Detection and Sustainable Crop Management 13th

Deep Learning Methods for Precise Sugarcane Disease Detectio...

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13th international Advanced Computing conference, IACC 2023

作者： Sharma, Davesh Kumar Punhani, Akash Department of Computer Science and Engineering SRM Institute of Science and Technology NCR Campus Modinagar Ghaziabad India

ISBN: (纸本)9783031567025

In the agricultural domain, sugarcane crops, like many others, are susceptible to diseases, posing a significant threat to both quality and quantity of production. Identifying and mitigating these diseases in their early stages are critical to averting financial losses for farmers. In response, researchers have turned to Artificial Intelligence (AI) techniques such as machine learning (ML) and Deep learning (DL) to analyze diverse agricultural data, including yield prediction, climate patterns, and soil quality, with disease prevention being a prime focus. this paper presents a thorough exploration of the effectiveness of a Deep learning-based Convolutional Neural Network (CNN) algorithm tailored for the detection of prevalent sugarcane diseases in India. Motivated by the rapid evolution of disease classes and farmers’ limited diagnostic skills, this study employs advanced deep learning and computer vision techniques. through image categorization into healthy and diseased groups, the trained model achieves an impressive 98.69% accuracy rate in sugarcane disease detection. Furthermore, to empower farmers, a web-based application is developed for ongoing disease monitoring. the paper suggests future research avenues, including user feedback integration and exploring the intersection of disease detection with agricultural productivity enhancement and price forecasting, thus enriching farmers’ decision-making processes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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Deep Reinforcement learning Method of Target Hunting for Multi-agents with Flocking Property 1

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proceedings of the 4th international conference on Neural Computing for Advanced applications, NCAA 2023

作者： Dong, Yujiao Cheng, Jin Wang, Qiqi School of Electrical Engineering University of Jinan Jinan250022 China Qilu Hospital of Shandong University Jinan250012 China

ISBN: (数字)9789819958443

ISBN: (纸本)9789819958436

A multi-agent deep deterministic policy gradient method is proposed for the target hunting problem in this paper. the behaviours of target hunting and flocking are trained with the action experiences under random environment while the multi-agents explore the environment randomly. A danger zone model is designed to construct a reward function module for the hunting behaviour of the target, and an improved artificial potential energy function is designed to impel the agents to evolve to the flocking state. To evaluate the proposed method, experiment is simulated with gym. the experimental results show that the multi-agent hunting of dynamic targets with flocking property is successfully realized. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Gradient methods

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Research on Autonomous Vehicle Trajectory Planning for Complex Urban Environments 25

Research on Autonomous Vehicle Trajectory Planning for Compl...

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proceedings of the 2025 4th international conference on Big Data, Information and computer Network

作者： Bingbing Long Sixuan Leng Xingzhi Lin GuangXi Vocational Normal University Nanning Guangxi China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning Guangxi China GuangXi Vocational Normal University Nanning Guangxi China

ISBN: (纸本)9798400712425

In terms of the impact of complex urban environments on the trajectory planning of self-driving vehicles, there are issues such as the complexity of the urban traffic environment, the diversity of traffic participants, and the suddenness of traffic events. A solution for trajectory planning of self-driving vehicles is proposed in terms of improving travelling safety and enhancing traffic efficiency. In order to solve this problem, integrated environment sensing and path planning techniques are used to construct a comprehensive framework for autonomous driving trajectory planning. Using proximal policy optimisation and reinforcement learning methods to train the dynamic scenario trajectory planning strategy for self-driving cars, we obtain an economical trajectory planning strategy network that can take into account a variety of constraints, combine various sensors to obtain the information of the surrounding environment and combine with high-precision maps and advanced algorithms to sense and understand the road situation in real time, and make the corresponding decisions and control. the results of the study show that the research can improve the ability of self-driving vehicles to cope with complex urban environments and the safety and stability of driving.

关键词： Autonomous driving

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An Advanced Deep learning Approach for Dietary Recommendations using ROBERTA

An Advanced Deep Learning Approach for Dietary Recommendatio...

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Intelligent Technologies (CONIT), international conference on

作者： Bajjuri Usha Rani Terli Joshnavalli Betha Srikanth Reddy Atluri Sreelaasya Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

this project introduces a novel Food Recommendation System empowered by the Roberta model, a state-of-the-art transformer-based architecture in natural language processing. Leveraging Roberta’s advanced language understanding capabilities, our system aims to revolutionize the domain of dietary recommendations by providing personalized and context-aware suggestions to users. the Roberta model plays a pivotal role in capturing intricate textual nuances related to nutritional content, dietary preferences, and individual health profiles, thereby enhancing the accuracy and relevance of the recommendations. the methodology involves the integration of Roberta into the recommendation system, detailing the fine-tuning process and adaptation of the model to the unique challenges posed by dietary recommendation tasks. We explore the incorporation of relevant nutritional databases, ensuring that the Roberta model is well-versed in the domain-specific knowledge required for effective food suggestions. the system’s performance is evaluated through various metrics, showcasing its ability to outperform traditional rule-based and machine learning-based approaches in providing tailored dietary advice. Furthermore, this project presents insightful curves and analyses derived from the model’s training process, illustrating the learning trajectory, and highlighting key milestones in its proficiency. the experimental results demonstrate the system’s effectiveness in adapting to diverse user preferences and evolving dietary trends, establishing its potential to positively impact users’ health and well-being. Ultimately, our Food Recommendation System, driven by the Roberta model, signifies a significant advancement in the fusion of deep learning and nutritional science. the methodology curves presented herein offer a comprehensive understanding of the model’s learning dynamics, emphasizing its role in revolutionizing the landscape of personalized dietary recommendations. the promising res

关键词： Measurement Training Deep learning Adaptation models Accuracy Semantics Natural language processing

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IMU Based Human Gestures Recognition using Deep learning for Wearable Devices 22

IMU Based Human Gestures Recognition using Deep Learning for...

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proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Lin Fu School of Design Innovation Victoria University of Wellington New Zealand

ISBN: (纸本)9781450398343

In recent years, the emergence of wearable devices, such as smartwatches and smart glasses. Based on this, human gesture recognition (HGR) for wearable devices has been a popular topic in the community of computer vision. Previous human gesture recognition approaches are mainly based on statistical methods, the breakthrough of deep learning enables researchers to make use of different neural network architectures to learn features of the human gestures that are collected from wearable devices. Most of these methods are based on the convolutional neural network, which extracts deep features from local manna. However, this way impedes the models from learning global information. To solve the problem, we propose a novel approach that leverages the attention mechanism to capture global information about human gestures. We conduct experiments on a benchmark, and the experimental results demonstrate that our proposed method is superior to the other baselines.

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4th international Workshop on Human Brain and Artificial Intelligence, HBAI 2024

4th International Workshop on Human Brain and Artificial Int...

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4th international Workshop on Human Brain and Artificial Intelligence, HBAI 2024

ISBN: (纸本)9789819640003

the proceedings contain 28 papers. the special focus in this conference is on Human Brain and Artificial Intelligence. the topics include: Uncovering Cognitive Taskonomy through Transfer learning in Masked Autoencoder-Based fMRI Reconstruction;Interpersonal Relationship Analysis with Dyadic EEG Signals via learning Spatial-Temporal Patterns;Effect of Music Training in Neural Responses to Emotional Speech Prosody: Insights from EEG and Brain Network Analysis;potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony;Exploring EEG-Based Neural Correlates of Multivariate Ordinal Emotion Representations;the Co-varying Multimodal Pattern in Treatment-Resistant and Non-treatment-Resistant Schizophrenia;investigating the Dynamics of Seizure Neuroactivities Using Hidden Markov Model;suppressing Seizure via Optimal Electrical Stimulation to the Hub of Epileptic Brain Network;SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition;BL-BERT: Extracting Body Language from Behavior Sequences in Freely Moving Mice;Benchmarking Neural Decoding Backbones Towards Enhanced On-Edge iBCI applications;Enhanced Local Attention with Deep Neural Networks for EEG Decoding;Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal Recognition;Active Urination Detection Using EEG Based on FBCNet;D2CAN: Domain-Guided Contrastive Adversarial Network for EEG-Based Cross-Subject Cognitive Workload Decoding;group-Specific Fusion Model and Its application in Identifying Multimodal Co-varying Diagnostic Patterns for Psychiatric Disorders;multi-category Brain Tumor Segmentation via Multi-scale and Cross-category Relation Modeling;consistent Brain Age Difference in Childhood Autism Spectrum Disorder and its Subtypes;Brain-Aware Readout Layers in GNNs: Advancing Alzheimer’s Early Detection and Neuroimaging;TSICNet: Importance of Connectome Information for Epilepsy Classification;a Brain-Inspired Distr

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