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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4961-4970 订阅

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DEEP learning applications TO CLASSIFY CROSS-TOPIC NATURAL LANGUAGE TEXTS BASED ON thEIR ARGUMENTATION 4

DEEP LEARNING APPLICATIONS TO CLASSIFY CROSS-TOPIC NATURAL L...

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4th international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Kale, Chaitanya P. William, P. Ogudo, Kingsley A. Khalaf, Osamah Ibrahim Department of Mechatronics Sanjivani College of Engineering Savitribai Phule Pune University Pune India Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune India Depending of Electrical and Electronics Engineering FEBE University of Johannesburg Johannesburg South Africa Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq

ISBN: (纸本)9781839539954

this assignment poses a challenging but essential problem for the rapidly developing discipline of natural language processing (NLP), which tries to classify information that is contested within texts that cover a variety of subject domains. the assignment in question seeks to classify information that is contested within texts. Enhanced Deep Inference for Text Segmentation (EDITS) is the name of the model that was used in this research project to demonstrate how deep learning can be integrated with more conventional approaches to machine learning such as Random Forest, Support Vector Machine, and Naive Bayes. the purpose of this research was to demonstrate the usefulness of deep learning in practice. the naive Bayes algorithm, support vector machines, and random forests are some of the approaches that fall under this category. In order to extract features and semantic information from the writings that are a part of the Quantitative Argumentation Dataset (QuAD), which is made of writings on a wide variety of topics, we use methods such as term frequency-inverse document frequency (TF-IDF) analysis. this allows us to extract information from the writings that is relevant to our research. this allows us to extract information from the texts with a greater degree of precision. Because of this, we are able to glean information from the sentences in a more accurate and exact manner. In this study, we compare a deep learning model called EDITS, which was developed specifically for lectures, to models that are more traditional. EDITS was created with the intention of improving speech recognition. this research expands our understanding of argumentation in a wide variety of contexts and demonstrates how cross-topic dispute may be classified via EDITS in real-world scenarios. © the Institution of Engineering & Technology 2023.

关键词： Adversarial machine learning

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A CNN and HBA Based Approach for Grape Disease Identification

A CNN and HBA Based Approach for Grape Disease Identificatio...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： K Vishnuvarthan R Renugadevi S Santhi Department of Computer Science and Engineering KIT – Kalaignarkarunanidhi Institute Of Technology Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Grapes are among the most extensively cultivated fruit crops globally, with over 10,000 varieties. they serve as a significant economic resource for many nations due to their diverse applications, including wine production, fresh juice consumption, raisin production, and juice manufacturing. However, grape diseases pose a severe threat to the viticulture industry, leading to substantial financial losses and a decline in wine quality. Manual evaluation is the foundation of traditional disease detection techniques, which are error-prone in addition to saving time. this highlights the need for an automated system capable of analyzing plant health, identifying diseases, and addressing undernourishment efficiently and reliably. Accurate disease diagnosis and timely control measures are crucial for mitigating these challenges. Many scholars have studied various algorithms such as CNN, SVM, Neural Networks, Random Forests, etc., yet each has disadvantages. So we have proposed an automated deep learning-based approach that integrates the Honey Badger Algorithm (HBA) with Convolutional Neural Networks (CNN) for the detection of grape diseases. A phenomenal 95% accuracy rate was attained by the prototype after it was trained on a dataset of 10,000 photos. Its performance was evaluated across various grape varieties and environmental conditions, demonstrating its robustness and adaptability.

关键词： Deep learning Support vector machines Sentiment analysis Accuracy Pipelines Crops Production Convolutional neural networks Sustainable development Diseases

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Explainable AI for Predictive Analytics on Employee Attrition 4th

Explainable AI for Predictive Analytics on Employee Attritio...

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4th international conference on Soft Computing and its Engineering applications (icSoftComp)

作者： Das, Sandip Sayan, Chakraborty Sajjan, Gairik Majumder, Soumi Dey, Nilanjan Tavares, Joao Manuel R. S. JIS Univ Dept CSE Kolkata India Swami Vivekananda Univ Dept CSE Kolkata India Future Inst Engn & Management Dept Business Adm Kolkata India Techno Int New Town Dept CSE Kolkata India Univ Porto Inst Ciencia Inovacao Engn Mecan Engn Dept Engn Mecan Fac Engn Porto Portugal

ISBN: (纸本)9783031276088;9783031276095

Employees are the key to an organization's success. An employee can directly affect the productivity of any organization. Hence, retention of the employees becomes great challenge for every organization. Artificial Intelligence is extensively used in such cases to analyze the problem behind a company's low retention rate of valuable employees. the analysis can help organizations to build up strategies to deal with the problems of employees leaving an organization. the current work aims to detect key features or main reasons behind an employee's key decision to stay or leave the organization. Primarily in this work machine learning is used extensively to create a create a prediction model on the collected data from human resources of different organization. In addition, the proposed framework uses explainable Artificial Intelligence methods such as SHAPley Additive exPlanations and Local Interpretable Model-Agnostic Explanations to analyze the key factors behind employees leaving the organizations. the current work managed to accurately create a prediction model with a score of 96% and detect three key features behind employee's decision of staying or leaving any organization.

关键词： Random Forest Explainable AI Employee retention Machine learning LIME SHAP

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A Neural Approach Towards Real-time Management for Integrated Energy System Incorporating Carbon Trading and Electrical Vehicle Scheduling 4th

A Neural Approach Towards Real-time Management for Integrat...

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

作者： Li, Yiying Wang, Bo Zhang, Lijun Liu, Lei Fan, Huijin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing Huazhong University of Science and Technology Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819958467

this paper proposes a real-time integrated energy system (IES) management approach which aims at promoting overall energy efficiency, increasing renewable energy penetration, and smoothing load fluctuation. the electric vehicles (EVs) charging scheduling is incorporated into the IES management, where the uncertain arrivals and departures of multiple EVs are considered as a stochastic but flexible load to the IES. Furthermore, towards the carbon neutralization target, a carbon emissions trading mechanism is introduced into the IES management to incentivize the system to operate in an eco-friendlier manner. To tackle the computational complexity induced by the stochastic and intermittent nature of the renewable energy sources and EVs load, the scheduling of the IES is realized in a neural network based real-time manner, driven by a deep reinforcement learning approach that guarantees safe training and operation. the case study verifies the effectiveness of the proposed approach. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

ISBN: (纸本)9783031837890

the proceedings contain 109 papers. the special focus in this conference is on Advanced Network Technologies and Intelligent Computing. the topics include: Exploratory Study on Different Transformer Models;visionLingua: Empowering Multimodal Understanding with Cutting-Edge Language Models;human Action Recognition Using Mediapipe Holistic Keypoints: A Deep learning Approach;machine learning for Waste-to-Energy: optimization and Predictive Analytics;enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model;fault Detection in Semiconductor Manufacturing Using Advanced Machine learning Techniques with Interpretability;fine-Tuning RetinaNet for Few-Shot Fruit Detection;Smart Street: AI-Powered Traffic Flow Enhancement with Adaptive Signal Control;a Hybrid Machine learning Approach for Accurate Milk Quality Classification;Designing VoiceGPT Chatterbox Concierge: A Personal AI Assistant for Daily Services Using Supervised Machine learning Based Speech Recognition Techniques;improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection;Enhancing Customer Segmentation and Behavior Prediction through Machine learning and Medium Gaussian SVM;Grapevine SDLC Model for Real-Time Fake News Classification;Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity;MoReco: AI Model Recommendation and optimization for B5G Networks;file Prefetching Accuracy Enhancement Using Deep learning Approaches;Mamba Based Conditional GAN for Optical Cloud Removal of Satellite Images;a Hybrid Quantum Machine learning Model for Multi-class Classifier;adaptive Resource Management in Massive Machine-Type Communications Using Reinforcement learning;Advanced Machine learning and Features Based Data Analytics of Electricity Consumption in USA and India;cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal Neural Networks;deep Featu

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Reward Prediction for Representation learning and Reward Shaping 13th

Reward Prediction for Representation Learning and Reward Sha...

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13th international Joint conference on Computational Intelligence (IJCCI) / 13th international conference on Evolutionary Computation theory and applications (ECTA)

作者： Hlynsson, Hlynur David Wiskott, Laurenz Ruhr Univ Bochum Inst Neuroinformat Univ Str 150 Bochum Germany

ISBN: (纸本)9789897585340

One of the fundamental challenges in reinforcement learning (RL) is the one of data efficiency: modern algorithms require a very large number of training samples, especially compared to humans, for solving environments with high-dimensional observations. the severity of this problem is increased when the reward signal is sparse. In this work, we propose learning a state representation in a self-supervised manner for reward prediction. the reward predictor learns to estimate either a raw or a smoothed version of the true reward signal in an environment with a single terminating goal state. We augment the training of out-of-the-box RL agents in single-goal environments with visual inputs by shaping the reward using our reward predictor during policy learning. Using our representation for preprocessing high-dimensional observations, as well as using the predictor for reward shaping, is shown to facilitate faster learning of Actor Critic using Kronecker-factored Trust Region and Proximal Policy optimization.

关键词： Reinforcement learning Representation learning Deep learning Machine learning

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csl-MTFL: Multi-task Feature learning with Joint Correlation Structure learning for Alzheimer’s Disease Cognitive Performance Prediction 9th

csl-MTFL: Multi-task Feature Learning with Joint Correlatio...

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19th international conference on Advanced Data Mining and applications, ADMA 2023

作者： Liang, Wei Zhang, Kai Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466700

Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing regularization have been widely investigated on cognitive performance prediction tasks. Although they have proved to achieve improved performance compared with single-task learning, the major challenges are still not fully resolved. they involve how to capture the non-linear correlation among the tasks or features, and how to introduce the learned correlation for guiding the MTFL learning. To resolve these challenges, we introduce a correlation structure learning method through self-attention learning and sequence learning for jointly capturing the complicated but more flexible relationship for features and tasks, respectively. Moreover, we develop a dual graph regularization to encode the inherent correlation and an efficient optimization algorithm for solving the nonsmooth objective function. Extensive results on the ADNI dataset demonstrate that the proposed joint training framework outperforms existing methods and achieves state-of-the-art prediction performance of AD. Specifically, the proposed algorithm achieves an nMSE (normalized Mean Squared Error)/wR (weighted R-value) of 3.808/0.438, obtaining a relative improvement of 3.84 % /7.35 % compared with the MTFL method. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurodegenerative diseases

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Atomic Orbital Search—A Soft Computing Approach to optimization 7th

Atomic Orbital Search—A Soft Computing Approach to Optimiza...

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7th international conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Jyothi, Kilari Dubey, R.B. SRM University Delhi-NCR Haryana Sonepat India

ISBN: (纸本)9789819799251

this paper provides a survey of research work done in different areas using atomic orbital search optimization algorithm (AOS). AOS is one of the new meta-heuristic’s algorithms for optimization problems. this algorithm’s core idea is based on some quantum mechanics concepts and the quantum-based atomic model, where the electrons surrounding the nucleus are generally arranged in a point of view. AOS is one of the more effective modern meta-heuristic optimization algorithms. It can be used in a variety of optimization problems in several domains, including flexible job shop scheduling, cloud computing, machine learning, engineering design, wireless networking, power and energy. this paper illustrates literature on AOS and its variant multiobjective atomic orbital search (MOAOS). Potential directions for future research on AOS and MOAOS are suggested in the conclusions, which center on the current works on the subject, after its applications, assessment and evaluation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Job shop scheduling

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Machine learning applications for Online Partial Discharge Detection, Classification, and Localization in Power Transformers: A Review

Machine Learning Applications for Online Partial Discharge D...

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Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Ameera Tag Shady S. Refaat Sayed Mohammad Kameli Mohammad AlShaikh Saleh Electrical and Computer Engineering Department Texas A&M University at Qatar Doha Qatar Department of Engineering School of Physics Engineering and Computer Science University of Hertfordshire Department of Electrical and Computer Engineering Texas A&M University College Station USA

the power transformer is a crucial asset and a fundamental component of the power grid. Assets undergo aging due to the stresses present in insulation materials. Partial discharges (PDs) are the most common fault source in power transformers and an excellent indicator of aging. the detection, classification, and localization of PD activities in power transformers are persisting challenges, while techniques utilizing machine learning (ML) are widely sought to deal with those challenges. Existing ML techniques show promising results with an elevated level of accuracy and precision. However, there is a lack of conventional ML-based real-time monitoring capability. therefore, this paper presents a comprehensive review of the application of ML techniques for online PD activity detection, classification, and localization in power transformers, focusing on supervised, unsupervised, semi-supervised, and reinforcement learning techniques. In addition, this paper explores the challenges, future trends, perspectives, and outlook of machine learning for online transformer fault analysis.

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Dynamic Energy Management for EV-Integrated Grids Using Reinforcement learning

Dynamic Energy Management for EV-Integrated Grids Using Rein...

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Electrical Machines and Drives (ICEMD), international conference on

作者： Saeed Izadi Behnam Zaker Mahnaz Izadi Department of Electrical Engineering Hamedan Azad University Hamedan Iran Department of Automotive Engineering Politecnico di Torino Torino Italy Department of Electrical Engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

the rapid expansion of electric vehicles (EVs) in modern power grids introduces opportunities and challenges for grid operators. Efficiently managing the bidirectional energy flow between EVs and the grid is essential to mitigate voltage deviations, minimize energy losses, and enhance overall grid stability. Traditional optimization methods often fail to adapt to dynamic grid conditions, limiting their effectiveness in real-time applications. To address these challenges, this paper proposes a hybrid scheduling framework that combines classical optimization techniques with a novel reinforcement learning (RL)-based control system for EV energy management. the proposed RL model optimizes charging and discharging schedules by leveraging real-time grid data such as voltage profiles, load variations, and state-of-charge (SOC) of connected EVs. Unlike conventional methods, the RL approach continuously learns optimal control strategies through interaction with the grid environment, offering adaptive solutions under varying load scenarios. Simulation results demonstrate that the proposed method significantly reduces energy losses, improves voltage stability, and balances grid demand, even during peak load conditions. Integrating advanced ML techniques with grid optimization represents a transformative step toward sustainable energy systems that efficiently integrate EVs.

关键词： Schedules Energy loss Simulation Power system dynamics Reinforcement learning Power system stability Stability analysis Real-time systems Vehicle dynamics thermal stability

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